→ EO Open Science 2017

Lightning Talks

16:50 - 18:00

• 16:50 - Complex SAR Data Processing For Evaluation Of Environmental Changes
  Frasheri, Neki; Beqiraj, Gudar; Bushati, Salvatore - Academy of Sciences of Albania, Albania

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  Past experiments with Sentinel-1 interferograms applied in PreAdriatic Depression area in Albania gave some intriguing results, characterized by lack of fringes in southern part and dominance of fringes in northern part but from images with 12 days if difference. Such results indicated a strong impact of land coverage in interferograms, making difficult the evaluation of possible subsidence in beaches of Adriatic Sea. In this paper we present results from Sentinel-1 interferograms based on different polarisations and comparison with land coverage features obtained from combination of coherence, intensity averages and intensity differences. Results show a strong impact of land coverage spatial and temporal variations, as well as new information on subsidence areas. Processing of Sentinel-1 images was done with ESA’s SNAP software, post-processing of resulting images with GIMP, appliicable in framework of citizen science as well.

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• 16:53 - Trend analysis of vegetation index in different land use land cover (Case study: Iran).
  Fakharizadehshirazi, Elham (1,2); Sabziparvar, Ali akbar (2); Sodoudi, Sahar (1); Fallah hasanabadi, Bijan (1) - 1: Free university of Berlin, Germany; 2: Bu-Ali Sina University,Iran

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  Vegetation Is an Essential Element of the Land Surface System That Links Climate Change. Both Environmental and Anthropogenic Factors Are Effected on Vegetation Dynamics. There Are Several Remote Sensing Indices for Indicating Vegetation. Normalized Difference Vegetation Index (NDVI) Is a Common and Widely Used Index. NDVI Is Highly Sensitive to Ecosystem Conditions; Therefore, It Can Be Representative of Detecting Changes in Vegetation Activity. The Visible and near Infrared Bands on the Satellite Multi Spectral Sensors Have Monitored the Greenness of Vegetation. In This Research, We Present the Trend Analyses of 31 Years (1982-2012) Remote Sensing Vegetation Index Based on Long-term Time Series of NDVI Observation from the Global Inventory Modelling and Mapping Studies (GIMMS) Group Derived from NOAA AVHRR Imagery With 0.08 Degree Spatial Resolution over Iran (40 T0 65 East, 25- 45 North). Iran Has a Dry Climate Characterized by Long, Hot and Dry Summers. NDVI Has a Strong Seasonal Cycle, and in This Research, We Only Used the Mean Growth Season NDVI. We Depict Greening (NDVI Increase) and Browning (NDVI Decrease) Regions. NDVI, an Indicator of Vegetation Growth and Coverage, Has Been Described the Characteristics of Land Use and Land Cover. In This Research, We Also Have Investigated NDVI Changes in Different Land Use Land Cover to Find out the Relationship Between Land Use Land Cover and NDVI Trend. The Existence of Positive Autocorrelation in the Time Series Increases the Probability of Detecting Trends When Actually None Exist, and vice Versa. In This Study, the Effect of Autocorrelation on the Variance of the Mann-Kendall Trend Test Statistic Is Considered, Therefore We Have Used Modified Man-Kendall Method to Do Trend Analyses. According to the Results, Almost Regions Have Negative NDVI Trend and It Is Harmonic with Land Use Land Cover in Some Cases.

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• 16:56 - Crustal structure beneath Mt Cameroon region derived from gravity data
  Ngatchou Heutchi, Evariste - University of Yaounde 1, Cameroon

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  In the present study, gravity information is available for improving the understanding of the crustal structure and its relationship to regional tectonics environment beneath the large volcanic system called Mt Cameroon. The multi-scale wavelet analysis method is applied to separate the gravity fields. The logarithmic power spectrum method is used to estimate different depths of the gravity field source. The results show that the crustal structure is very complicated beneath Mt Cameroon area with the crustal density exhibiting lateral inhomogeneity. The lateral discontinuities of density structure causes undulations of the gravity anomaly field whose complexity can be an indicator of past crustal instability. The Buea-Tiko region appears to be the most tectonic active zone in the Mt Cameroon area. The upper and middle crusts consist of many small-scale faults, uplifts and depressions. In the lower crust, these small-scale tectonic units disappear gradually, and replaced by large-scale units. The gravity anomalies in upper and middle crusts are correlated with geological and topographic features on the surface. Compared with the crust, the structure is relatively simple in uppermost mantle. The earthquakes occurred predominantly in upper and middle crusts, their epicenters are limited in transitional regions between high gravity anomaly and low gravity anomaly. The earthquake occurrence as well as complicated gravity behavior may be related to the Upwelling of high density magmatic materials and asthenosphere heat flow materials beneath Mt Cameroon. The overall results, in a good agreement with previous findings, show the performance of the wavelet-based filter in the possibility of getting a multi-resolution analysis and the study of structures using gravity data.

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• 16:59 - Deep Learning Techniques For The Information Extraction From Large Earth Observation Data
  Licciardi, Giorgio (1,2); Dalla Mura, Mauro (2); Chanussot, Jocelyn (2,3) - 1: Research Consortium Hypatia, Italy; 2: Gipsa Lab Grenoble, France; 3: University of Iceland, Iceland

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  Creation of valuable content from large and growing volume of EO derived data is a challenge for Research organizations, governments and companies. Several approaches have been proposed in the literature for the information-extraction from EO data, among which Deep learning (DL) algorithms have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. With the term Deep Learning is intended a set of machine learning systems (usually neural networks) with multiple layers. Deep learning involves a class of models, which try to hierarchically learn deep features of input data with very deep neural networks, typically deeper than three layers. The network is first layer-wise initialized via unsupervised training and then tuned in a supervised manner. In this scheme, high-level features can be learned from low-level ones, whereas the proper features can be formulated for pattern classification in the end. Thus, the use of more than three layers permits to extract more abstract, invariant features of data, and have been shown to yield promising performance in many fields of remote sensing including classification or regression tasks. By analyzing the practical demands in Earth Observation applications, in this paper we show how the use of deep learning approaches can be used anywhere in remote sensing analysis: from pre-processing to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding. Different examples showing the use of DL techniques applied to different stages of EO data processing will be presented. In a first example, it will be shown an application for the spectral compression and the noise suppression of hyperspectral images [1][2]. Then, an example of how the DL can extract relevant information from data acquired from different sensors will be presented [3][4]. Finally, a large time-series dataset of meteorological images is processed with DL in order to extract relevant features [5]. REFERENCES [1] G. Licciardi, J. Chanussot, A. Piscini, “Spectral compression of hyperspectral images by means of nonlinear principal component analysis decorrelation”, ICIP 2014, 27-30 october 2014, Paris, France. [2] G. Licciardi, J. Chanussot, ‘Nonlinear PCA for visible and thermal hyperspectral image quality enhancement”, IEEE Geoscience and Remote Sensing Letters, Volume 12, issue 6, pages 1228-1231. 2015. [3] G. Licciardi, M. M. Khan, J. Chanussot, A. Montanvert, L. Condat, C. Jutten, “Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction”, EURASIP Journal of Advances in Signal Processing, 2012, 2012:207 [4] G. Licciardi, R. G. Avezzano, F. Del Frate, G. Schiavon, J. Chanussot “A Novel Approach to Polarimetric SAR Data Procesing Based on Nonlinear PCA”. Pattern Recognition, Volume 47, issue 5, pp: 1953-1967, 2014. [5] G. A. Licciardi, R. Dambreville , J. Chanussot, S. Dubost “Spatio-temporal pattern recognition and nonlinear PCA for global horizontal irradiance forecasting”, IEEE Geoscience and Remote Sensing Letters, Volume 12, issue 2, pages 284-288, 2014.

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• 17:02 - Contribution of the New satellites (Sentinel-1, Sentinel-2 and SPOT-6) to the Coastal Vegetation Monitoring in the Pays de Brest (France).
  Talab Ou Ali, Halima; Niculescu, Simona - LETG UBO, France

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  The significant economic, societal and environmental changes that have occurred worldwide and at different regional levels account for the strong interest that scientists are currently showing in issues related to coastal land cover changes. The intense urban extension, the development of its related infrastructures and town planning, the deep transformations of agricultural practices and the increasingly intensive farming and exploitation of natural resources have all led to considerable changes in the coastal ecosystems. Our paper aims mainly at finding a methodological solution applicable for the processing of several heterogeneous coastal ecosystem parameters, which would allow their description in their full complexity. This complexity is the result of both local variations of ecological conditions and different anthropogenic factors having direct and indirect influences on plant communities’ dynamics of these ecosystems. Although understanding the dynamics which governs the changes occurring in these “patchwork” areas is still a difficult task (natural evolution of plant communities, human activities,…), we begin our paper by an interferogram calculation method of the main types of vegetation to achieve the coherence of a multi-temporal Sentinel-1 radar image series, in SLC format (C band, VV and VH polarization), between 2015 and 2016. We then proceed to calculating radar backscatter coefficient based on Sentinel-1 images in GRD format. The approach adopted relies on analysis of relations between state of different vegetation ecosystems and radar response, while establishing a link between the physical coherence responses and backscatter coefficients with the vegetation parameters (phenology, structure…) along with soil humidity due to precipitations. Assuming that backscatter coefficient may be considered suggestive of degree of vegetation development and of its various phenological stages, we were thus able to determine the different temporal patterns of the various classes of coastal vegetation. In addition to the spectral and spatial dimensions, the time component is a priceless source of information in terms of plant resource monitoring and management and land cover dynamics follow-up. Our study of radar image series collected especially during the growth stage enables us to improve the recognition of the main types of vegetation by relying on the temporal dynamics of the various classes of vegetation. The temporal analyses have proven that there is not only one date which would allow a satisfactory characterization of all vegetation classes. The temporal dimension, represented by the seasonal dynamic, is thus a vital component of any thorough inventory and analysis of the coastal vegetation ecosystems. A combination of coherence and intensity images complements the Sentinel-1 radar image processing, since the use of the two images may be employed for land cover classification. Second, our findings concern combinations of data collected and recorded by Sentinel- 1 using different optical satellite sensors (Sentinel-2 and Spot-6) to improve the accuracy of recognition and mapping of the main classes of vegetation in the Pays de Brest, going as plant formation. For this first stage, the findings show average accuracy levels for SPOT-6 and Sentinel-2 image classification. Furthermore, the combination of the three types of data insures excellent multi-sensor classification accuracy (higher than 92%).

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• 17:05 - Evaluation of multi-source Earth Observation data exploitation for monitoring intensive crop farming.
  Arcorace, Mauro; Delgado Blasco, Jose Manuel; Cuccu, Roberto; Sabatino, Giovanni; Rivolta, Giancarlo - Progressive Systems Srl, Parco Scientifico di Tor Vergata, 00133, Rome, Italy

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  The exploitation of satellite Earth Observation (EO) data has already played key roles in the agricultural sector where remote sensing contributions are traditionally strong. EO data can be used in fact to monitor fields in the cropping season, to estimate crop production loss due to drought or excessive rainfall, to estimate the humidity of the soil, to identify change in type of vegetation and to monitor deforestation. In this domain, we are exploring new capabilities on multi-source EO data exploitation for precise farming applications. The main goal of this applied research is to build a dedicated and customizable portfolio of services that can be easily adopted by private or public sector stakeholders to support agricultural needs at local scale. Having a versatile EO-based agricultural monitoring service in place would be, in fact, a key instrument to provide valuable and reliable information on crop conditions, promote the use of EO data within the industry, address crop field monitoring needs over extensive rural areas and guide local stakeholders to identify yield and production losses. A preliminary study over selected sites in Italy has been carried out to demonstrate the feasibility of prototypical tools for agricultural monitoring. In particular, in order to evaluate the applicability of remotely sensed indicators to enable cost reduction of agricultural activities, multiple investigations have been performed over different types of crop field. Thanks to the Google Earth Engine cloud-based platform, the information derived from Earth Observations, such as multi sensor (e.g. Sentinel-2, Landsat-8, ProbaV) low and medium resolution time series analysis, have been used to monitor crop growth dynamics and to better understand environmental behaviours meaningful for agriculture. First results, have been shown that by means of these satellite based measurements it is possible to identify a correlation with the crop season of different cultivation fields and to analyse other relevant features. For example, thanks to an integrated analysis of vegetation and water indexes, it is possible to assess crop field status and to estimate harvesting period and annual irrigation schedule. Furthermore, multi temporal remote sensed surface temperature analysis, retrieved from Landsat 8 Thermal Infrared (TIRS) are also useful to identify seasonal temperature fluctuation across the years. Besides creating value for the farmers, such a type of analysis can be used for other purposes as well, e.g. for measuring economic loss in case of disaster events. Precise farming is expected to be widely adopted by the agricultural community in the near future, as the continuously growing remote sensing data sources (satellite, UAV) and ground observations will enable the development of new ad-hoc services. This work is intended to pave the way towards future implementations of support services for agricultural industry and farmers, providing a wide range of satellite-derived indicators for precise farming applications, such as crop disease identification, pollution monitoring, soil mapping and yield prediction. The results derived from this study are expected to provide supportive evidence of the potential value of the envisioned support services for local-scale applications.

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• 17:08 - Lena Delta Water Bodies Mapping And Level Heights Determination Based On Remote Sensing Data
  Volynetc, Aleksandr - St. Petersburg University, Russian Federation

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  Low-lying permafrost-dominated Arctic river deltas are particularly sensitive to climate variability. This sensitivity is dramatically expressed in landscapes changes because of permafrost degradation (thermokarst and thermoabrasion processes) and river-ocean interactions changes due to rivers run-off increase. As thawing of permafrost may lead to the release of great amount of green-house gases and acceleration of climate change, close monitoring of permafrost affected regions dynamics using remotely sensed data becomes a prominent instrument for analysis of climate variability. One of the main features of permafrost degradation is thermokarst - expressed in formation, growth, decreasing and vanishing of thermokarst lakes. Thus water objects quantity size and level change can be considered as important indicators for the water balance and for frozen ground mutability in corresponding areas. The region of interest of this study is situated in continuous permafrost zone Lena Delta, which is the largest delta in the Arctic region with an area exceeds 32 000 km². It includes about 60 000 lakes, most of them are impacted by thermokarst, and numerous branches of the Lena River. On the base of high resolution (5m) multi-year RapidEye satellite imagery was created a map of water bodies in the Lena Delta. The first step of mapping was preprocessing of satellite images, which includes atmospheric correction of obtained images, orthorectification and projection in geodetic coordinate system UTM 52N (zone of the eastern part of Lena delta) using software provided by PCI Geomatics. On the next step near-infrared channels of appropriate ones were superimposed on each other, with the condition based on suitable ground reflectance values, and a binary raster image of water bodies was obtained. Received raster was filtered using different methods to remove noises and then converted in vector scheme. Thus vector map of lakes and channels in the Lena Delta was obtained, which contains about 34 000 objects. This map was overlaid by footprints of laser altimeter on board of ICESat, provides an unprecedented set of global elevation measurements of the Earth, and water level heights of appropriate water bodies were estimated. As a result was received sufficiently detailed map of Lena Delta water objects with elevations of several lakes and inclinations of main river channels.

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• 17:11 - Datacube Analytics for the Digital Earth: Challenges and Opportunities
  Mantovani, Simone (1); Natali, Stefano (1); Barboni, Damiano (1); Steer, Adam (2); Evans, Ben (2); Hogan, Patrick (3); Baumann, Peter (4) - 1: MEEO, Italy; 2: National Computational Infrastructure - The Australian National University, Australia; 3: NASA Ames Research Center, USA; 4: JACOBS University, Bremen, Germany

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  Recently, the term datacube is receiving increasing attention as it has the potential of greatly simplifying “Big Earth Data” services for users by providing massive spatio-temporal data in an analysis-ready way. The Datacube Manifesto [1] provides a concise, crisp definition of datacubes, based on the consolidated experience of project partners in datacube modeling (query languages, architectures, standards development, …) and in operation of Petascale datacube services at some of data centers worldwide. A number of datacube-aware platforms and services have emerged that enable a new collaborative approach for analysing the vast quantities of satellite imagery and other Earth Observations, making it quicker and easier to explore a time series of image data stored in global or regional datacubes. In this context, the European Space Agency and European Commission H2020-funded projects ([2], [3]) bring together multiple organisations in Europe, Australia and United States to allow federated data holdings to be analysed using web-based access to petabytes of multidimensional geospatial datasets. The aim is to create and ensure that these large spatial data sources can be accessed based on OGC Web Coverage Service (WCS) and Web Coverage Processing Service (WCPS) standards. Unlike Web Mapping Service (WMS) which returns spatial data as an image or ‘static map’, WC(P)S returns data in its raw form, with its original semantics enabling further data processing or the building of web applications, while at the same time the data volume transferred is minimised. WCS provides access to the full range of geospatial data served from a web server and allows for requesting only a subset of the data. A WCS supports slice and trim operations, where either the data dimension (slice) or the data extent (trim) is reduced. WCPS is an extension of the WCS 2.0 core specification and allows the user to craft queries to be run on the data using a text based query language, similar to SQL. This allows the user to not only limit the data transfer to the area they are interested in, but also web-based, on-demand data processing. In this study, we provide an overview of the existing datacubes (EarthServer-2 datacubes, Sentinel Datacube, European and Australian Landsat Datacubes, …), how the regional datacube structures differ, how interoperability is enabled through standards, and finally how the datacubes can be visualized on a virtual globe (ESA-NASA WebWorldWind) based on a WC(P)S query via any standard internet browser. The current study is co-financed by the European Space Agency under the MaaS project (ESRIN Contract No. 4000114186/15/I-LG) and the European Union’s Horizon 2020 research and innovation programme under the EarthServer-2 project (Grant Agreement No. 654367). [1] The Datacube Manifesto (http://earthserver.eu/sites/default/files/upload_by_users/The-Datacube-Manifesto.pdf) [2] MEA as a Service (http://eodatacube.eu) [3] EarthServer-2 (http://www.earthserver.eu)

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• 17:14 - EarthStartsBeating: an innovative Earth Observation divulgation project
  Palumbo, Giovanna; Tarchini, Salvatore; Cerreti, Fiammetta; Cadau, Enrico Giuseppe; Iannone, Rosario Quirino; Marino`, Fernando - SerCo S.p.a, Italy

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  This abstract proposes and describes a project aimed at the divulgation of scientific topics, mainly written for a non-expert audience. The outreach activities are performed through the writing of articles published on a website named EarthStartsBeating (https://earthstartsbeating.com/) and launched since early 2015. In line with the Copernicus free and open data access idea, EarthStartsBeating is intended to present and promote the multiple exploitation areas of the Copernicus Sentinels Earth Observation (EO) data, reaching the far-end users, i.e. the European citizens, drawing their attention to a broader usage of Earth Observation data. The proposed articles cover therefore various scientific disciplines and simple curiosities about the Earth natural/human processes through a weekly publication of Sentinels' eye-catching products elaborations. The publication of EO images fosters the understanding of subjects such as geological and atmospheric phenomena, including also the anthropic changes caused by the human activities. The main goal of the contributors is thus to work on communication and storytelling, approaching the information with a direct and simple style without neglecting the main scientific principles of the Remote Sensing. EarthStartsBeating website is structured in four thematic sections: • The 'Sentinels' section, which shows EO images of natural and anthropic phenomena, following the most significant events occurring throughout the Earth. The relevant material is then classified by three tags corresponding to the name of the Sentinels data exploited for the elaborations (i.e. Sentinel-1, Sentinel-2, and Sentinel-3). In this section, events like hurricanes, floods, forest fires, volcano eruptions are treated. • The 'Exploration' section is meant to re-experience the paths of the various historical explorers thanks to the eyes of the Sentinels. The first story tells about some stages of James Cook's first journey that led to the exploration and discovery of a great part of the South Pacific Ocean and Australia. • The 'Expert User' section is a more technical area where simple and basic scripts written in java/python where technical articles about the elaboration of EO images are reported. • The 'Interesting facts' section collects analysis done with data coming mainly from ESA not-Sentinels missions like PROBA-V. We present some examples of the main exposed themes, demonstrating that the proposed website represents an attractive tool not only for gathering information but also for the development of a forum in which to discuss the most relevant aspects linked to our Planet.

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• 17:17 - EO with Sentinel2A: a school-work path way experience
  Amici, Stefania (1); Stelitano, Dario (1); D'addezio, Giuliana (1); Vento, Paola (2); Acocella, Francesca (2); Giorgetti, Giorgio (2); Rocchi, Giorgia (2); Sbrenni, Eleonora (2); Serenellini, Luca (2) - 1: Istituto Nazionale di Geofisica e Vulcanologia; 2: Liceo Scientifico Stanislao Cannizzaro

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  School path way experience known as alternanza scuola-lavoro (ASL) is an innovative learning-teaching module at Italian college schools. As detailed within the "Good School" law ( La Buona Scuola 107- 2015) it is aligned with the principle of open school. The idea is to anticipate the contact of students with working world providing them new skills and directions for future work choices within a sort of internship project with a private or public body. In this context Istituto Nazionale di Geofisica e Vulcanologia offers a list of projects within geophysical application. For the first time, researchers from INGV wanted to challenge college students on Remote Sensing to test their response and potential receptivity. The project, titled "Earth Observation with satellite: the case of burn areas" was selected by five students, here co-authoring, from Liceo Scientifico Cannizzaro in Rome. The school-work path way experience has been hold at INGV in Rome for a total of 40 hours (contact and not contact) during which the students have been guided to how to: • identify a problem • create an initial research questions • establish basic theoretical knowledge on satellite remote sensing • identify the algorithms suitable for solving the problem • select the satellite data (Sentinel2A) and the process tool (SNAP) • analyze and interpret data producing a scientific report As test case, two poorly characterized wildfires occurred in the area of Aidomaggiore (on 02 July 2016) , and Scano di Montiferro (on 24 August 2016 ) Sardinia Italy were selected. Images acquired by Seninel2A over the area were selected and used to produce NDVI and NBR map at 20m spatial resolution. The results have been summarized by students in a scientific report. Both students and researchers and professor as well considered the experience very positive and perhaps we have initiated the concept that "Junior Remote Sensing specialist" can be potentially extended to college students

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• 17:20 - High School Students and Sentinels’ Data: Our Experiment
  Liberti, Gian Luigi (1); Ciotoli, Elisa (2); Bilhac, Marius (2); Liberti, Juliette (3); Capannolo, Edoardo (2); Ciobano, Rafaela (2); Picone, Massimiliano (2) - 1: ISAC-CNR, Via Fosso del Cavaliere 100, I-00133, Rome, Italy; 2: Liceo Scientifico Statale "Louis Pasteur" V. Giuseppe Barellai, 130 - 00135 Rome, Italy; 3: Liceo Scientifico Statale "Isacco Newton" V.le Manzoni 47, 00185 Roma

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  Within the frame of the recent introduction (Alternanza Scuola Lavoro www.istruzione.it/alternanza/index.shtml) in the Italian public school system of compulsory stages in private and public enterprises, the possibility that high school students could contribute actively to a scientific project was explored. About 30 high school students from the 3rd and 4th year (roughly 16-18 years old) from two Scientific High Schools in Rome, “Louis Pasteur” (www.liceopasteur.it) and “Isacco Netwon” (www.liceonewtonroma.gov.it), were involved in different phases of a study performed at the Institute for Atmospheric Sciences and Climate of the Italian National Research Council (ISAC-CNR www.isac.cnr.it). The study was done in response to the ITT Sea-ice cloud screening for Copernicus Sentinel-3 Sea and Land Surface Temperature Radiometer issued by EUMETSAT. The project required to produce Probability Density Functions (PDF) for a three classes (Clear Sky/Cloud/Sea Ice) Bayesian classification of the Sentinel 3 SLSTR data over polar oceans. Student were involved mainly in two different job tasks: first, they were asked to organize a set of relevant reviewed literature, about 140 among journal articles, Technical Reports and Algorithm Theoretical Basis Documents (ATBD), and to summarize the main information used for cloud-sea ice detection from previous studies. Secondly, their contribution consisted in selecting and documenting study cases, used as a reference dataset for validation and tuning of the PDF’s in different phases of their development. Job Organization: Different approaches for the job organization were tested, such as not only a tutor vs. students formation, but also a peer-to-peer one. The students had the possibility to work and to develop their abilities both at the work place and remotely (their school or their houses). Even if they could also work far from the tutor, there was an intense exchange of information. The study cases’ selection was based on the use of these tools: SNAP (step.esa.int), a Cloud area to share results and data, Microsoft Office. This required the students to become familiar with them. As far as SNAP is concerned, they learnt how to manage it mostly taking advantage of the online tutorials. Earth Observation (EO) data used in this project were Sentinel-3(A) SLSTR L1 (scihub.copernicus.eu) data and daily sea ice 1 km concentration charts (SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002), based mainly on observations from SAR, including the one on board of Sentinel 1. Furthermore, some examples of obtained results will be shown and discussed. Some difficulties emerged during the time the project was carried out. They were not due to the tools or to the EO data but mostly to the novelty of the situation the students had to face. Their scholastic background revealed itself to be less important than a constructive attitude aimed at an efficient way to manage time and resources. In particular, the inability to, adequately, report the work done and to respect deadlines appeared as common weaknesses. Both students and project’s gained benefits will be discussed.

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• 17:23 - Communication Duration With Low Earth Orbiting Satellite
  Gupta, Lalit - Poornima Institute of Engineering and Technology Jaipur, India

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  Communication via satellite begins when the satellite is positioned in the desired orbital position. The satellite’s coverage area on the Earth depends on orbital parameters. Ground stations can communicate with LEO (Low Earth Orbiting) satellites only when the satellite is in their visibility region. The duration of the visibility and so the communication duration varies for each satellite pass at the ground station. For low cost LEO satellite ground stations in urban environment it will be a big challenge to ensure communication down to the horizon. The communication at low elevation angles can be hindered through natural barriers or will be interfered by man made noise. This paper discusses the variations of the communication duration between the ground station and LEO satellites and investigates if it is useful to support low elevation passes. For this paper data recorded at the Vienna satellite ground station within the Canadian space observation project “MOST” (Micro variability and Oscillations of Stars) are applied.

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• 17:26 - Earth Observation Projects for Maximum Education
  Fortunato, Ronald (1); Hogan, Patrick (2) - 1: Trillium Learning, USA; 2: NASA, United States of America

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  The World Bridge program integrates Earth observation technology to advance educational research as applied in the classroom. These projects dynamically design and implement Real-Time, Real-World Project-Based Learning into the local curriculum, and account for the classroom content needed to address academic standards. Three projects will be presented, all involving Earth observing web apps built entirely by students. One project involves the United Nation World Heritage sites that you can instantly zoom to and see in detail from a satellite view of the Earth. Another is a virtual globe web app for managing the water, sewer, power and road system for the city of Kodiak Alaska. Another project is a sophisticated monitoring system for measuring the Earth's magnetic field at 50Hz, at the nano-Gauss sensitivity for the x, y and z axes. The magnetic field has been show to become anomalous in the few days preceding an earthquake, measurable within a few hundred kilometers of the seismic hypo-center. This EO system is student designed, built and installed, and delivering data live via a virtual globe representing the exact location of the data. All of these Earth observing education projects are part of the World Bridge program, and entirely based on principles of open source and open data.

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• 17:29 - 3D Cave Mapping Applied to the "CAVES" and "PANGAEA" ESA Programs
  Santagata, Tommaso (1); Sauro, Francesco (1,2); De Waele, Jo (1,2); Bessone, Loredana (3) - 1: La Venta Esplorazioni Geografiche, Italy; 2: Department of Biological, Geological and Environmental Sciences, University of Bologna; 3: Directorate of Human Space Flight and Operations, European Space Agency, Linder Höhe, 51147 Köln, Germany

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  The PANGAEA (Planetary Analogue Geological and Astrobiological Exercise for Astronauts) and CAVES (Cooperative Adventure for Valuing and Exercising human behaviour and performance Skills) ESA training courses are designed to prepare European astronauts to become effective partners of planetary scientists and engeneers in designing for the next exploration missions and to give them a solid knoweledge in the geology of the solar system studying several caves, especially lava tubes, through geolgical field training courses and tests of new technologies. In recent years we have seen a remarkable developments of 3D mapping methods for cave surveys as cameras and softwares for digital photogrammetric and instruments as laser scanning or mobile mapping tools. During the 2016 CAVES training course in Sardinia (Italy), photogrammetry has been widely used in order to give astronauts a basic knoweledge of this 3D mapping technique and as a toll that can be used for documenting the surfaces of other planets during field geology activities. Photogrammetry allows to acquire metric data through the acquisition and analysis of a couple of frames that can be obtained using standard digital cameras. In 2017, laser scanning and UAV'S (Unmanned Aerial Vehicles) photogrammetry will be used during the PANGEA course to test new instruments to obtain 3D maps of the Corona lava tube on the Canarian island of Lanzarote (Spain) with the goal to realize virtual models that can be used to test rovers and to plan future space analogue missions. In both cases, the 3D models realized with photogrammetric and laser scanning technologies were subsequently analysed to obtain information about size, volume, shapes and morphologies of the detected surfaces. The aim of this work is to describe methods and technologies used during these tests and the results obtained.

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• 17:32 - A set of Software Tools supporting EO Satellites for Orbit and Instrument Swath Coverage
  Pinol Sole, Montserrat; Zundo, Michele - ESA/ESTEC, Netherlands, The

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  This paper presents the software applications for satellite orbit and instrument swath visualization distributed by the ESA-ESTEC EOP System Support Division to users part of the ESA Earth Observation Earth Explorer and Copernicus satellites community. The ESOV NG [REF 1] and EOMER [REF 2]) software applications can be used to perform mission analysis activities related to instrument swath coverage over regions of interest and ground station contact. These tools can and have been used in the preparatory feasibility studies (e.g. to analyse coverage and revisit time), to support downlink and ground station visibility analysis as well as support to Calibration and Validation activities e.g. to plan on-ground campaigns during satellite commissioning or scheduling of ground transponders. The Earth observation Swath and Orbit Visualization (ESOV NG) is a 2D orbit and swath visualization application, delivered with a predefined set of missions (Aeolus, Biomass, Cryosat , EarthCARE, MetOp-SG, Sentinel 1, 2, 3, 5p, 6, SMOS, SWARM), although it is possible to configure user-defined satellites. This tool is multi-platform, available for Mac OS X, Linux and Windows. EOMER (Earth Observation Mission Evaluation and Representation) is a Windows application for multi-satellite and swath visualization in 2D/3D, tailored to ESA Earth Observation missions (currently supporting Aeolus, Biomass, MetOp-SG, Sentinel 1, 2, 3, 5p). Both ESOV NG and EOMER applications allow the user to visualize satellite orbit ground-tracks and instrument swaths, and to calculate ground station visibility passes, times of overpass a given ground point and times when an instrument swath overlaps with a given region of interest. Regarding this last point, a dedicated command line executable program (ZoneOverPass [REF 3]) is available to obtain overpass tables of a given satellite ground-track or instrument swath over an area of interest. Finding opportunities of observations over a given area may be useful to search relevant time-tagged products or plan future on-ground campaigns. Further exploiting these features, the InstrCollocation tool [REF 3] provides a mechanism to identify collocation opportunities between two different instruments. It would benefit users involved in instrument calibration activities or interested in combining data from different types of products, acquired over the same geographical area within a given period of time between observations. The output information produced by the ZoneOverPass and InstrCollocation tools are provided in both tabular and graphical formats. The coherence and accuracy of the orbital and geometrical calculations within ESOV NG application and the ZoneOverPass and InstrCollocation tools is ensured by the use of embedded Earth Observation CFI Software libraries (EOCFI SW). The libraries are used to obtain orbit ground-track, instrument swath, time passes over selected area of interest or ground. The EOMER application instead makes use of the SatX and GanttX components developed by Taitus to support respectively the orbital calculations and its timeline visualisation. The use of common interfaces (orbit files, swath files, SCF segments export format from ESOV NG, KML Google Earth) is a key point to facilitate sharing the input data and the comparison of the output results across the various software applications. REFERENCES [REF 1] ESOV website: http://eop-cfi.esa.int/index.php/applications/esov [REF 2] EOMER website: http://eop-cfi.esa.int/index.php/applications/eomer [REF 3] ZONE_OVERPASS / INSTRUMENT_COLLOCATION website: http://eop-cfi.esa.int/index.php/applications/tools

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• 17:35 - Virtual Exploitation Environment Demonstration for Atmospheric Missions
  Natali, Stefano (1); Mantovani, Simone (1); Santillan, Daniel (2); Triebnig, Gerhard (2); Hirtl, Marcus (3); Lopes, Cristiano (4) - 1: SISTEMA gmbH, Austria; 2: EOX IT Services GmbH, Austria; 3: Zentralanstalt für Meteorologie und Geodynamik, Austria; 4: ESA ESRIN, Italy

  Show Abstract

  The scientific and industrial communities are being confronted with a strong increase of Earth Observation (EO) satellite missions and related data. This is in particular the case for the Atmospheric Sciences communities, with the upcoming Copernicus Sentinel-5 Precursor, Sentinel-4, -5 and -3, and ESA’s Earth Explorers scientific satellites ADM-Aeolus and EarthCARE. The challenge is not only to manage the large volume of data generated by each mission / sensor, but to allow users to analyze the data streams in near-real-time and for long term monitoring tasks. Creating synergies among the different datasets will be key to exploit the full potential of the available information. As a preparation activity supporting scientific data exploitation for Earth Explorer and Sentinel atmospheric missions, ESA funded the “Technology and Atmospheric Mission Platform” (TAMP) [1] [2] project, with the twofold aim of demonstrating (1) that multiple data sources (satellite-based data, numerical model data and ground measurements) can be simultaneously exploited by users (mainly scientists), and (2) that a fully virtualized environment (Virtual Research Environment, VRE) that allows avoiding downloading all data locally, and retrieving only the processing results is the optimal solution. With the “Virtual Exploitation Environment Demonstration for Atmospheric Missions” (VEEDAM) project, the concept of VRE is further extended: a Jupyter notebook interface has been deployed aside of the data, providing the users with all data access and processing tools already available within the TAMP platform as libraries for further exploitation; thus the user can exploit the potentialities of the platform, including the freedom of writing and running own code directly on the VRE; moreover the user has the possibility to interact with other existing VREs using external data and processing resources. Finally the interactive 3D visualization capabilities of TAMP have been further evolved providing geographic (latitude, longitude, height) slicing capabilities. This interactive poster presents the VEEDAM capabilities, allowing the attendants of EO Science 2.0 to directly experience both the data visualization and exploitation potentialities of TAMP. [1] TAMP platform landing page http://vtpip.zamg.ac.at/ [2] TAMP introductory video https://www.youtube.com/watch?v=xWiy8h1oXQY

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• 17:38 - Grand Ethiopian Renaissance Dam break scenario and expected effects over the archaeological sites
  Elshobaki, Mohamed (1); Elfadaly, Abdelaziz (2) - 1: Università degli Studi dell'Aquila-Italy; 2: Università degli Studi della Basilicata-Italy

  Show Abstract

  The Nile River flows for 6,700 kilometres through ten countries in north-eastern Africa. It is classified as one of the longest river in the world. Two main tributaries supply the discharge of the Nile being the White, and the Blue Nile. The Blue Nile currently attracts more attentions due to the construction of the Grand Ethiopian Renaissance Dam Project (GERDP). Large debate from the downstream countries (Egypt) has feared the consequences of such project. The GERDP left us with a huge concern in case of failure. Therefore, we provide a full numerical simulation based on the Shallow water mathematical model which solved by TELEMAC-2D provides the necessary water flow information in case of the Dam breaking. Satellite images data includes Sentinel1 and DEM data is used to supply the initial and boundary conditions of the simulations. Overall aim, the extracted simulation data are used to assess the potential impact of the GERDP failure on the archaeological area along the banks. Detect the expected environmental risks should supply the decision-makers with understandable information to protect the cultural heritage sites. Tsunami waves are expected.

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• 17:41 - Crowdsourcing EO datasets to improve cloud detection algorithms and land cover change
  Aleksandrov, Matej (1); Batič, Matej (1); Milčinski, Grega (1); See, Linda (2); Perger, Christoph (2); Moorthy, Inian (2); Fritz, Steffen (2) - 1: Sinergise, Slovenia; 2: International Institute for Applied System Analysis (IIASA)

  Show Abstract

  Involving citizens in science is gaining considerable traction of late. With positive examples (e.g. Geo-Wiki, FotoQuest Austria), a number of projects are exploring the options to engage the public in contributing to scientific research, often by asking participants to collect some data or validate some results. The International Institute for Applied Systems Analysis (IIASA), with extensive experience in crowdsourcing and gamification, has joined Sinergise, Copernicus Masters 2016 winners, to engage the public in an initiative involving ESA’s Sentinel-2 satellite imagery. Sentinel-2 imagery offers high revisit times and sufficient resolution for land change detection applications. Unfortunately, simple (but fast) algorithms often fail due to many false-positives: changes in clouds are perceived as land changes. The ability to discriminate of cloudy pixels is thus crucial for any automatic or semi-automatic solutions that detect land change. A plethora of algorithms to distinguish clouds in Sentinel-2 data are available. However, there is a need for better data on where and when clouds occur to help improve these algorithms. To overcome this current gap in the data, we are engaging the public in this task. Using a number of tools, developed at IIASA, and Sentinel Hub services, which provide fast access to the entire global archive of Sentinel-2 data, the aim is to obtain a large data resource of curated cloud classifications. The resulting dataset will be published as open data and made available through Geopedia platform. The gamified process will start by asking users if there are clouds on a small image (e.g. 8x8 pixels at the highest Sentinel-2 resolution of 10 m/px), which will provide us with a screening process to pinpoint cloudy areas, employing Picture Pile crowdsourcing game from IIASA. The next step will involve a more detailed workflow, as users will get a slightly larger image (e.g. 64x64 pixels) and will then be asked to delineate different types of clouds: opaque clouds (nothing is seen through the clouds), thick clouds (where the surface is still discernible through the clouds), and thin clouds (where the surface is unequivocally covered by a cloud); the rest of the image will be implicitly cloud-free. The resulting data will be made available through the Geopedia portal, both for exploring and downloading. This paper will demonstrate this process and show some results from a crowdsourcing campaign. The approach will also allow us to collect other datasets in a rapid and efficient manner. For example, using a slightly modified configuration, a similar workflow could be used to obtain a manually curated land cover classification data set, which could be used as training data for machine learning algorithms.  

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Exh/Demo/Exhibitions - TEP Demo

18:00 - 19:00

• 18:00 - Exhibitions - TEP Demos 1
  

  Show Abstract

  Exhibitions - TEP Demos 1

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Citizen Science

Chairs: Brovelli, Maria Antonia (Politecnico di Milano), Mathieu, Pierre Philippe (ESA-ESRIN)

08:30 - 10:30

• 08:30 - A new method and web tool for Map Assesment and Warping (MAW) and its application to OSM data.
  Brovelli, Maria Antonia; Prestifilippo, Gabriele; Zamboni, Giorgio - Politecnico di Milano, Italy

  Show Abstract

  OpenStreetMap (OSM) is currently the world's largest database, to which millions of volunteers contribute. By now, it has nearly 4 billion nodes, 400 million ways and almost 5 million spatial and logical relationships among the elements. This database is a precious source of information and the data it contains can be reused by everyone in accordance with its Open License, the Open Data Commons Open Database License (ODBL). Anyone is free to copy, distribute and adapt the data, providing the paternity to OSM and its contributors and guarantying that any alteration of the data is made available with the same license. Often the accuracy of the OSM data is called into question. This work tries to partly answer this question by providing a method for measuring the metric accuracy of a target layer, like for instance that corresponding to OSM buildings (the layer of building footprints), by comparison with a second reference layer, for instance an authoritative one, representing the ground truth. The method is therefore more general, allowing the evaluation of metric differences between two layers, but it can be applied to OSM data in order to check their quality. It is complemented by a web mapping tool, where users can upload their target and reference layers and have back all the relevant statistics. The prepared procedure also provides the ability to warp the target layer to the authoritative one with a least squares approach based on the affine or on multi-resolution spline transformation. The transformation parameters can then be applied to all layers (road, hydrography, etc) spatially homogeneous with the target one (i.e, the building footprints layer), making them spatially homogeneous to the reference. Besides the procedure and the web application, statistics for some large Italian and foreign cities will be shown (Milan, Paris, Berlin, S.Francisco, Boston), comparing OSM data with official data with accuracy at urban scale (from 1: 1000 to 1: 5000).

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• 09:00 - Citizen Scientists Measuring Australia’s Water Quality
  Anstee, Janet; Ford, Phillip; Malthus, Tim - CSIRO, Australia

  Show Abstract

  Water colour is a very informative indicator of the ecological state of marine and fresh-waters. Until recently, however, colour information has only been measurable with a suite of unwieldy scientific instruments. With the ubiquity of smartphones, several phone applications have been developed which offer the exciting prospect that water colour measurements and associated metadata can now be collected over wide spatial and temporal scales by citizen scientists. A new project starting in July 2017 aims to harness the enthusiasm of Australian citizen scientists interested in the water quality of their local regions. These individuals will generate a large pool of valid data to calibrate satellite information, and provide a synoptic overview of river, lake, and coastal water quality for natural resource management in Australia. The usefulness of the data for calibration of satellite remote sensing data and its applicability in water quality management such as an early warning system of harmful algal blooms will be assessed. Each participant will obtain a quantitative understanding of how local water bodies change seasonally and in response to short duration events like floods and cyclones. As Australia has vast underpopulated regions, a targeted group of citizen scientist will include indigenous rangers working in isolated regions in tropical Australia where scientific investigations are limited or non-existent. Once organisations and communities are engaged, CSIRO will provide training for the mobile phone application, deployment of a Secchi disk, use of a basic chemical water quality test kit and data transfer. Data will be uploaded by the participants and results will be returned to the users through a website and regular newsletters. This presentation will outline the early stages of implementation of the project, the benefit to citizen scientists in enhanced knowledge of water quality issues and environmental awareness, and the benefits provided in calibration and validation of satellite data.

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• 09:15 - Earth Science in Real Time
  Douglas, Elizabeth (1); Lukaszczyk, Agnieszka (2); Mascaro, Joseph (3) - 1: Planet Labs Germany GmbH, Germany; 2: Planet Labs Germany GmbH, Germany; 3: Planet Labs Germany GmbH, Germany

  Show Abstract

  The value of science and data has, as of late, been a hot button topic. Demonstrators are marching and headlines are being written about future government investment in data collection in support of science. And even while some governments are pulling back, we are in the midst of a global sensing revolution. Innovations in satellite and rocket manufacturing have ushered in a step-change: 2017 was the first year in history that more than 100 Earth observation satellites were launched in a single year. Today, most locations on the land surface now have their picture taken multiple times per day. Ecological, geological and human-caused changes on the Earth’s surface are visible like never before: the demographics of remote tropical trees, flowering, dropping their leaves, and flushing. Alpine lakes, dammed by glacial ice, draining and re-filling. Sand encircling Indian Ocean Islands, ebbing with seasonal currents. And yet, each of these dynamic, diverse Earth systems are under intense and increasing threat from climate change, human expansion and loss of biological diversity. If it is coupled with accessibility for scientific use, this global sensing revolution gives the scientific community an unprecedented opportunity to conduct Earth science in real time. So far, the results are encouraging, with more and more commercial data becoming accessible to scientists and researchers. But, this new vantage comes with challenges: multiple sensors and platforms, operated by scores of agencies, mean analyses must be increasingly interoperable to keep pace. The study of change on our Planet must be rigorously tuned to technological changes in the very sensors that allow us to track that change. How we meet the challenge of synthesizing and drawing insights from these evolving data streams—as a global community—will determine our planet’s fate.

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• 09:30 - Including participative observation into environmental sciences : the Simplex system
  Mangin, Antoine (1); Rueda, Sebastien (1); Serra, Romain (1); Vincent, Chloé (1); Hembise, Odile (1); Baudin, Thomas (2); Riddell, Mike (3) - 1: ACRI, France; 2: Synext, France; 3: Université Internationale de la Mer - Cagnes/Mer, France

  Show Abstract

  In 2010 has been launched a participative system (web-based - meduse.acri.fr ) to report on presence/absence of jellyfish on French Riviera. All citizens, locals and tourists, were invited to report on jellyfishes. The result is double : i) a near-real-time mapping of risk, widely open to public, to manage their recreative bathing and ii) a mean to get statistical data, useful to carry out scientific studies of correlation of jellyfish presence and specific environmental conditions (that can be observed in situ and from space) ; scientific papers have been published making use of this “crowd” data and, by this, have demonstrated the quality of this approach for scientific purposes. This precursor system has been fully adopted by a very large public and can reasonably considered as a deep success (around 30.000 observations reported – that makes this data set the largest in Europe regarding jellyfish - more than 1,3 millions of connections, 7.500 registered observers…). This reporting/mapping system is still largely alive and is regularly promoted by public media (mainly for public health purpose). Based on this (unexpectedly large) success, and lessons learnt regarding acceptance of public to this type of monitoring, we have developed a “generic” system (Simplex (TM)) based on the contribution of public, or semi-public for scientific applications. The development of this system is supported by the French Ministry of Environment and allows any type of environmental monitoring in complement to other types of observation (EO, modeling, in situ…). SimplexTM consists in three complementary branches i) one generic and simple smartphone application for an observing cohort, ii) one web-GIS allowing management of this cohort by a coordinator and crossings with external input (environmental and socio-economic) and iii) NRT direct access to Copernicus (EO and models) data at the time/location of the observer and for the coordinator. Simplex(TM) is presently in beta-version and under test for four applications with various stakeholders (sea mammals monitoring, macro-wastes at sea, …). Results and possible benefit for EO validation will be presented at the EO Open Science workshop.

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• 09:45 - The E2mC Project: An Innovative Approach to Combine Social Media and Crowdsourcing for Rapid Mapping
  Corsi, Marco (1); Grandoni, Domenico (1); De Vendictis, Laura (1); Francalanci, Chiara (2); Pernici, Barbara (2); Scalia, Gabriele (2); Fernandez-Marquez, Jose Luis (3); Mondardini, Rosy (3) - 1: e-GEOS, Italy; 2: Politecnico di Milano, Italy; 3: University of Geneve, Switzerland

  Show Abstract

  The goal of the E2mC project is to demonstrate feasibility and usefulness of the integrating social media analysis and crowdsourced information within both the Rapid Mapping and Early Warning Components of Copernicus Emergency Management Service (EMS). Since 2012, the Copernicus EMS provides timely information for emergency response in relation to different types of disasters, as well as prevention, preparedness, response and recovery activities. It operates through four modules (Rapid Mapping, Risk & Recovery, European Forest Fire Information System (EFFIS) and European Flood Awareness System (EFAS)) that constitute to two main Service Components: Mapping and Early Warning. Through these two components, Copernicus EMS supports crisis managers, Civil Protection authorities and humanitarian aid actors dealing with natural disasters, manmade emergency situations, and humanitarian crises during the different phases of crisis management. The experience gained in these recent years of operational services has highlighted the need to enhance the service performance of both the Components, in terms of timeliness, quality and production capacity. Relating to the Mapping Component, satellite images are the key source for the maps production. One of the main user requirements of the EMS is to receive first crisis information within the first 24 hours after the disaster, while today it is not unusual to experience delays up to 72 hours, mainly due to the availability of the first usable post-event satellite image, caused by various casues, including satellite tasking and orbital constraints, bad weather conditions, and late activation. Moreover, the crisis maps, purely based on satellite information, have known quality limitations due to the physical constraints of satellite acquisitions (e.g. resolution or near-nadiral view) that negatively affect accuracy. Social media have proven particularly valuable in collecting information in emergency situations, such as in earthquakes, in particular in very destructive ones such as Haiti and Nepal, and in large floods and hurricanes, such as Hurricane Matthew and floods in the Philippines, in the different phases of such events. Preliminary analyses and surveys conducted in the requirements analysis phase of the project have highlighted that the operators involved in the crisis map production during an event (Rapid Mapping service) use social media information gathered manually, mainly with internet search and download. A fundamental innovation with E2MC is to provide a technology platform that supports the automated retrieval and analysis of social media information, combined with crowdsourcing, with the general goal of improving the quality and dependability of the information provided to professional users within the Copernicus network. This paper discusses the E2mC approach to the integration of social media and crowdsourcing within Copernicus rapid mapping and early warning service components. It discusses the functional architecture of the platform that will be developed by the E2mC team to support this integration. Preliminary results are discussed providing insights on the type, quality, amount and timeliness of the information that can be found on social media during an emergency and on the tasks that are best candidate for crowdsourcing.

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• 10:00 - Developing Methods To Use Citizen Observations As A Part Of A Long-Term Efficient Monitoring System
  Pyhälahti, Timo; Lindholm, Matti; Korhonen, Sami; Bruun, Eeva; Heinilä, Kirsikka; Alasalmi, Hanna; Junttila, Sofia; Lehto, Samuli; Keto, Vesa - Finnish Environment Institute SYKE, Finland

  Show Abstract

  Data sources for sustainable long term environmental monitoring system for any official or operational use should be reliable, consistent and unbiased both in spatial distribution of observations and observed values distriribution domain. Citizen observations (CO) have challenges to meet any of these requirements. However, there is a potential for both cost-effective data gathering and citizen involvement with co-operation and information dissemination benefits beyond acquiring environmental data. In fact, at current state of technology the problem is not acquisition of georeferenced data from mobile communication devices, rather the problem is how to motivate observers to commit to the information provision activity among other possible competing activities. With outlined process of introducing participatory citizen observations, the motivation of observers and the expectations of data gathering organizations can meet. By committing to extended Open311 information exchange practices the questionnaires for formalizing the provided CO data, similar questions can be provided across different user platforms and interfaces, supporting multiple languages. Practices of ensuring service discovery, data availability and quality are outlined for multi-organization framework. For water qality related CO measurements with Secchi3000 technology are reviewed with potential for satellite Earth Observation (EO) synergy.

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Citizen Science

Chairs: Brovelli, Maria Antonia (Politecnico di Milano), Mathieu, Pierre Philippe (ESA-ESRIN)

11:15 - 13:00

• 11:15 - The Cli-Mate? app.
  Soukup, Petr; Moullard, Olivier - University of Westminster, United Kingdom

  Show Abstract

  This presentation will document the design and development of a practical solution to climate change: the Cli-Mate? app. Cli-Mate? is a social network service for people interested about climate change. The application allows users to record and share environmental data using their iPhones, acting as a bridge between scientists and citizens concerned about climate change. Data collected through the Cli-Mate? app could form a much needed local record of climate change worldwide, which could help scientists to validate observations made by satellites and help them to make predictions about climate change.

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• 11:30 - Flowered-Geodbapp: An Application Based On Crowd-Generating Data Using Sentinel2 Imagery
  Deflorio, Anna Maria (2); La Mantia, Claudio (2); Melis, Maria Teresa (1); Dessì, Francesco (1); Loddo, Paolo (2); Da Pelo, Stefania (1); Ghiglieri, Giorgio (1); Tesfaw Hailu, Binyam (3); Kalegele, Khamisi (4); Mwasi, Benjamin (5) - 1: TeleGIS Laboratory, Dept. of Chemical and Geological Sciences, University of Cagliari, via Trentino 51, 09127 Cagliari, Italy; 2: Planetek Italia SRL, via Massaua, 12 70132 Bari, Italy; 3: School of Earth Sciences, University of Addis Ababa, College of Natural Sciences, P.O. Box 1176 Addis Ababa, Ethiopia; 4: Nelson Mandela African Istitution of Science and Technology, P.O. Box 447, Tangeru Campus, Arusha, Tanzania; 5: School of Environmental Studies, University of Eldoret P.O Box 1125 – 30100, Eldoret, Kenya

  Show Abstract

  This study is part of the EU H2020 research Project FLOWERED (de-FLuoridation technologies for imprOving quality of WatEr and agRo-animal products along the East African Rift Valley in the context of aDaptation to climate change). FLOWERED project aims to develop technologies and methodologies at cross-boundary catchment scales to manage the risks associated with high Fluoride water supply in Africa, focusing on three representative test areas along the African Rift Valley (i.e. Ethiopia, Kenya and Tanzania), characterized by high fluoride contents in waters and soils, water scarcity, overexploitation of groundwater and high vulnerability to risks arising from climate change, as drought and desertification. It also is empowering local communities to take responsibility for the integrated-sustainability of the natural resources, growing national and international environmental priorities, enhancing transboundary cooperation and promoting local ownership based on a scientific and technological approach. Within the FLOWERED project, the transition from the land cover to the land use and water use maps is provided through the development of a mobile application (FLOWERED-GeoDBapp ). It is dedicated to the collection of local geo-information on land use, water uses, irrigation systems, household features, use of drinking water and the other information needful for the specific knowledge of water supply involving local communities through participative approach. This system is structured to be populated, through an action of crowd-generating data by local communities (students and people involved mainly by NGOs). The SHAREGEODBapp is proposed as an innovative tool for water management and agriculture institutions at regional and local level.

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• 11:45 - Exploring the potential use of radar and optical data for Natural Flood Management
  Caccia, Michele (1); De Avila Siqueira, Andreia (1); Valcarce-Diñeiro, Rubén (2) - 1: CGI, Italy; 2: CGI, UK

  Show Abstract

  According to SEPA, 2015, Natural Flood Management (NFM) involves techniques that aim to work with natural hydrological and morphological processes, features and characteristics to manage the sources and pathways of flood waters. These techniques include restoration, enhancement and alteration of natural features and characteristics, but exclude traditional flood defence engineering that works against or disrupts these natural processes. Surface runoff is one key component impacting floods in a catchment area. Changes in the environment caused by human activities, changes to rainfall patterns caused by climate change as well as overall changes in the land surface can greatly contribute to runoff increase. Traditional runoff models use a number of different variables e.g. soil type, ground cover type and rainfall to estimate the runoff for a given area. Earth observation (EO) data has been widely used to monitor environmental variables including surface runoff (Gajbhiye, 2015). EO data applications are well-known to be a very reliable and cost-effective alternative to the conventional methods of monitoring and modelling environmental changes. The present study aims to demonstrate the effectiveness use of Sentinel-1 and Sentinel-2 data in addition to other datasets (DEM and VHR image) to map and monitor areas where runoff hazard is present. A Vulnerability Index is proposed based on value-added information originated from EO data. The proof of concept was carried out in the Hendred Farm (West Thames catchment area, United Kingdom, UK) during the 2016 growing season (March to September). The methodology involved the generation of two Land Cover thematic layers using a combination of co-polarised and cross-polarised images, and extracting the interferometric coherence and intensity over Single Look Complex product pairs from Sentinel-1 images. Sentinel-2 images from two distinct dates during the growing season (6 June and 15 August) were used to generate Vegetation Indexes (NDVI, NDWI, LAI, FAPAR and FCOVER). A slope thematic layer was created using a DEM (SRTM 1-sec resolution). Furthermore, a spatial framework was proposed and tillage distribution and direction thematic layer was created using a very high resolution image (WV3 – 04 May 2016). Classification maps were produced by integrating the different thematic layers and presenting runoff hazard spatial distribution as high, medium and low. Even though the study area was limited, it is believed that the proposed methodology is able to demonstrate that runoff hazard areas can be identified and monitored using radar and optical images over time. The real value of being able to assess and monitor runoff hazard is when large catchment areas are considered and traditional methods of monitoring are not cost-effective. Thus, the future steps of this study is to extend the methodology described here to provide a high level assessment of wider areas prior taking more detailed NFM actions.

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• 12:00 - Citizen Science and Crowdsourcing to Support Mobility Decision-Making in Large Metropolitan Areas: A Big Data approach
  Mazumdar, Suvodeep; Lanfranchi, Vitaveska; Wang, Zhibao; Simanjuntak, Humasak; Bai, Lu; Ireson, Neil; Ciravegna, Fabio - University of Sheffield, United Kingdom

  Show Abstract

  By the year 2050, global population living in metropolitan areas is estimated to grow significantly and is expected to cause enormous strain on infrastructure and resources. Metropolitan areas are typically very large areas with dense city centers and large expanding suburbs. With limited resources and rising population and pollution concerns, such areas need to grow in an efficient, sustainable and resilient way. A significant concern is how transport and mobility in such areas can scale up with such increasing demands. To this end, the Seta project is currently developing Big Data solutions to create a technology and methodology that will address these challenges and change the way mobility is organised, monitored and planned in such areas. The project collects massive volumes of mobility data via crowdsourcing, physical sensors, video cameras, and environmental sensors. The data is linked, fused and exploited to model mobility with a precision, granularity and dynamicity that is impossible with today's technologies. Essential to the success of the project is the key role played by citizens and communities. The project exploits different crowdsourcing mechanisms such as participatory and opportunistic to provide massive volumes of Volunteered Geographic Information (VGI). In this talk, we will discuss how hundreds of thousands of users, throughout three use case cities in Europe (Birmingham, UK; Santander, Spain; and Turin, Italy) contribute to opportunistically providing VGI on motorised and non-motorised mobility via mobile applications. While crowdsourced motorised mobility data informs various aspects of traffic and infrastructure management such as road occupancy, vehicle speed, pollution estimation and so on, crowdsourced non-motorised mobility provides significant insights into land use and function as well as how citizens and communities organise their daily commutes, activities and lifestyle. At the core of the project is a citizens observatories approach, where citizens can actively provide real-time information about traffic and mobility conditions and issues. This talk will present how such massive volumes of data is collected, processed and analysed to provide exploratory analysis for decision support. We will share our initial results and insights following the first phase of evaluations in the three cities and discuss how we plan the next phase of development in the project.

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• 12:15 - Monitoring the flowering of Invasive Alien Plants with Sentinel 2A/B and Citizen Science data
  César de Sá, Nuno (1,2); Sillero, Neftalí (3); Gil, Artur (4); Marchante, Elizabete (1); Marchante, Hélia (1,2) - 1: Centre for Functional Ecology, University of Coimbra, Portugal; 2: Coimbra College of Agriculture, Polytechnic Institute of Coimbra; 3: Research Center for Geo-space Science, University of Porto; 4: Centre for Ecology, Evolution and Environmental Changes, University of Azores

  Show Abstract

  Biological invasions by Invasive Alien Species (IAS) are not only one of the greatest threats to biodiversity and ecosystems functioning worldwide but also a potential health hazard for humans, with extensive economical costs with EU estimating up to 12 Billion € per year. Reducing costs and increasing the efficiency of IAS monitoring is vital for a sustainable strategy to counter biological invasions. Free and open access to Earth Observation data coupled with Citizen Science and data gathering platforms can provide a unique opportunity to curb these costs. Furthermore, these programmes contribute to increase citizen awareness and participation in the topic of IAS monitoring which is crucial for prevention and early detection. The web-platform invasoras.pt has been an example of success in increasing citizen awareness and participation in this topic, specifically towards invasive alien plants. Besides being part of a wider countywide set of activities that encourage active action by citizens it also allows the collection and storage of Volunteered Geographic Information (VGI) of invasive plants. This data is validated by experts and made totally available for public use. Coupling this continuous and increasing collection of data with freely available Earth Observation products offers is a great opportunity to increase the ability of monitoring IAP at very low operational costs. The availability of free Earth Observation products for continuous monitoring and their consistent improvement in recent years is a unique opportunity to improve and increase the scope of IAP monitoring. As a case study we’ve chosen three of the worst IAP in Portugal: Acacia longifolia, Acacia melanoxylon and Acacia dealbata and Acacia saligna. All these are characterized by very strong flowering events that occurring during Portuguese winter. The sheer intensity of their flowering facilitates their visibility in the field and hypothetically also through Earth Observation satellites. Furthermore, a biocontrol has been introduced recently in the country which is expected to seriously disrupt the flower production of A. longifolia. As such it is significant for future monitoring of this biocontrol expansion to establish if flowering is or isn’t detectable by satellite Earth Observation. While previous Earth Observation missions as the Landsat already offered the opportunity for monitoring IAP but they were hindered by the lower spatial resolutions and revisit time. Especially during winter, cloud cover is very prevalent. Therefore Sentinel 2A and 2B satellites are a unique opportunity for detection of these IAP because they offer both an increased spatial resolution as well as a smaller revisit time. In this research we showcase the possibility of using VGI to validate detection of these four IAP throughout various seasons using Sentinel 2A and 2B satellites. To do this, we pre-selected areas of known homogeneous and monospecific stands of these species and evaluated their detectability in the various seasons which are validated by the VGI. It is an example of how free-to-use EO data and simple citizen data gathering platforms can easily contribute to improve the monitoring of IAS at very large scales.

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• 12:30 - Picture Pile: A citizen science tool for rapid post-disaster damage assessment using satellite imagery
  Danylo, Olha (1); Sturn, Tobias (1); Giovando, Cristiano (2); Moorthy, Inian (1); Fritz, Steffen (1); See, Linda (1); Kapur, Ravi (3); Girardot, Blake (2); Ajmar, Andrea (4); Giulio Tonolo, Fabio (4); Reinicke, Tobias (3); Mathieu, Pierre Philippe (5); Fraisl, Dilek (1); Duerauer, Martina (1) - 1: International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria; 2: Humanitarian OpenStreetMap Team; 3: Imperative Space; 4: Information Technology for Humanitarian Assistance Cooperation and Action (ITHACA), Turin Italy; 5: European Space Agency (ESA), ESRIN, Frascati, Italy

  Show Abstract

  Citizen science, crowdsourcing and volunteered geographic information have become important components of participatory scientific research. Within the domain of post-disaster damage assessment, crowdsourcing can provide data to assist humanitarian organizations in their relief and recovery efforts. Several citizen science powered tools already exist for data collection, including those that support visual image interpretation and online interactive mapping. One example of such a tool is Picture Pile, which is a cross-platform application designed to be a generic and flexible way to ingest satellite imagery for rapid classification. As part of the ESA’s Crowd4Sat initiative led by Imperative Space, this study demonstrates how satellite imagery coupled with a crowdsourcing-based application (Picture Pile) can support humanitarian efforts. We demonstrate how satellite image interpretation tasks within Picture Pile can be crowdsourced in a demonstration case that uses imagery before and after Hurricane Matthew, which affected large regions of Haiti in September 2016. Volunteers were asked a simple yes/no question about what they could see with regards to building damage from the hurricane in images before and after this event. Since the task is simple and clearly formulated, a rapid review of large areas was done with the help of 135 volunteers who completed 120,000+ tasks within a few days of the start of the campaign. We present the latest results and discuss the lessons learned from the campaign which ran in May 2017. We found that most volunteers showed a high agreement rate with our experts, supporting the validity of such a crowd-driven approach for rapid post-disaster damage assessments. The proposed approach brings satellite imagery to volunteers and can be used to detect various features in different disaster events such as damaged buildings, flooded areas, areas burned by wildfires, damaged roads, and storms, among others. These features are easily recognizable from very high resolution satellite imagery, even to the lesser trained eyes of new participants. Other than collecting data, this approach also helps to increase citizen awareness of natural disasters and provides everyone with a unique opportunity to contribute directly to relief efforts. Picture Pile is intended to supplement existing approaches for post-disaster damage assessment and can be used by different networks of volunteers (e.g. the Humanitarian OpenStreetMap Team) to assess damage rapidly and to create up-to-date maps for timely response to disaster events.

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• 12:45 - SnapPlanet - Tell Your Stories From A New Perspective
  Gasperi, Jérôme (1); Manaud, Nicolas (2) - 1: SnapPlanet, France; 2: Space Frog Design, France

  Show Abstract

  SnapPlanet is on a mission to democratise the access and use of Earth Observation data in an easy, affordable and enjoyable way. At its core, SnapPlanet provides a “photographic centric” mobile application to create beautiful images of the Earth from space within seconds, thanks to the freely available ten meters Sentinel-2 imagery. These data ensure a free high-resolution snapping service for all users. These “snaps” can be annotated, commented and shared, making SnapPlanet a great tool to raise awareness of our living planet. Thus, by combining the ever-increasing availability of Earth observation images with the advantages of a social network for everyone, SnapPlanet wants to drive new uses of EO data to empower citizens, journalists, scientists to tell stories that can have a positive impact on society and environment on a global scale.

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Atmospheric Correction

Chairs: Mangin, Antoine (ACRI), Arino, Olivier (ESA- ESRIN)

14:00 - 15:15

• 14:00 - SEOM project for an operational atmospheric correction for Sentinel-2 above coastal and inland waters
  Mangin, Antoine (1); Serra, R. (1); Vincent, C. (1); Loisel, H. (2); Jamet, C. (2); Ngoc, Dinh (2,3); Fell, F. (4); Lafrance, B. (5); Aznay, O. (5) - 1: ACRI, France; 2: LOG (France); 3: VAST (Vietnam); 4: Informus (Germany); 5: C-S (France)

  Show Abstract

  In 2016 has been launched a scientific project supported by ESA in order to study the atmospheric correction for Sentinel-2 above coastal and inland waters. When successful, the overall idea is to made this algorithm available to the coastal community who is showing an increasing interest for Sentinel-2 through public tool (e.g. SNAP) and later on to allow systematic processing of L2A products dedicated to coastal zone in complement to land products. A consortium has been selected including ACRI, LOG, Informus and C-S. Each of the partners is bringing a particular expertise to each module of the whole processing chain: gaseous absorption correction, aerosols scattering, cloud/cloud shadow/land/water classifications, detection of topographic effects and, more specific to the water part and its interface to land; sun glint detection and correction, white caps and adjacency effects. After scientific evaluation of possible modules, first validation and a first round of implementation, the project is now entering into validation phase. Rationale for algorithm selections and validation plan and objectives will be presented.

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• 14:20 - MeetC2: multi-scales atmospheric corrections for the Sentinels 2
  Saulquin, Bertrand; Martin-Lauzer, Francois-Regis - ACRI-ST, France

  Show Abstract

  From the Top Of Atmosphere (TOA) observations and in coastal areas, unmixing the high spatial-frequency water signal from the low spatial-frequency atmospheric signal is challenging as multiple sets of solution of vector {ρaer (λ),ρw(λ),Ttot(λ)} are possible for a single set of TOA(λ) observations. Aerosol spatial-scales are between one to tens of kilometres, while in the same time in coastal areas, water signal spatial-scales are between tens of meters to hundreds of meters. These differences are particularly interesting to be exploited for unmixing the water and the atmospheric signal in coastal areas, where the “black pixel hypothesis”= null contribution of the water, is not valid and the shape of the water and the aerosol signals in the infrared may be very similar, leading to inversion errors. The inversion scheme itself of the existing processors such as the OL2 OLCI processor addresses partially the reliability of the estimates. For example, aerosol optical thicknesses and types are estimated regardless the fact that it will result later in the processor in negative water reflectance estimates: the inversion has converged towards a local minimum. The final consequence for the users is an over-flagging of the products in coastal areas and for the spatial agencies an under-optimisation of the level 2. To limit the influence of this bad conditioned system, we use here the Maximum a posteriori (MAP) criterion to add to the minimization cost function a priori onto the variable to be estimated. MeetC2 is a Level 2 prototype processor for the Sentinels 2 based onto both a spatial-downscaling analysis and a probabilistic inversion scheme. The TOA signal is estimated using LUTs (interpolated on the fly) generated with the radiative transfer codes OSOAA and SMART-G. The spatial downscaling approach relies onto the estimation of the parameters using an ensemble of pixels, decreasing for each scale from 6 km to 600m resolution. The spatial continuity of the atmospheric components between scales is here modelled using 4 directional gradients onto the inversed atmospheric variables. The high resolution water signal is finally obtained by the subtraction of the high resolution S2 observations with the low resolution atmospheric signal. The validation results include 64 matchups on 6 Aeronet-OC sites and MOBY. We characterise also in this presentation the added-value of the spatial-regularisation terms in the inversion. The V1 of the MeetC2 processor, written in python, is parallelised and is able to process one S2 image at 60m resolution in 2-3 minutes (resp. 10m resolution in 15 minutes using 16 CPUs). Sub-region extraction is also possible to enhance the computation time. Figure 1: Functional scheme for the MeetC2 algorithm based onto i/ the Maximum a Posteriori (MAP) criterion for convergence, as much as possible, towards non-negative and realistic ρw and ii/ spatial gradient constraints onto the atmospheric parameter estimates. Sentinel 2 Image over the Laguna of Venice, 20160827, AAOT Aeronet-OC site.

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• 14:40 - iCOR in coastal, transitional and inland waters : application to Sentinel-2
  De Keukelaere, Liesbeth (1); Sterckx, Sindy (1); Adriaensen, Stefan (1); Knaeps, Els (1); Reusen, Ils (1); Giardino, C (2); Bresciani, M. (2); Hunter, P. (3); Van der Zande, D. (4); Vaiciute, D. (5) - 1: Vlaams Instituut voor Technologisch Onderzoek (VITO), Belgium; 2: Consiglio Nazionale delle Ricerche – Instituto per il rilevamento elettromagnetico dell’ambiente (CNR-IREA), Italy; 3: University of Stirling, UK; 4: Royal Belgian Institute of Natural Sciences (RBINS), Belgium; 5: Klaipeda University, Lithuania

  Show Abstract

  The iCOR image correction, previously known as OPERA (Sterckx et al., 2015), is a scene and sensor generic atmospheric correction algorithm which can process images containing land and water pixels (coastal, transitional or inland waters). ICOR has the advantage that elevation, air-water interfaces and adjacency effects are considered and no assumptions are made on the turbidity of the water. Through the use of a single atmospheric correction implementation, discontinuities in the reflectance between land and the highly dynamic water areas are reduced. iCOR contains an image-based aerosol optical thickness retrieval module, based on the method developed by Guanter et al. (2007), and allows to perform a simplified adjacency correction over land using fixed ranges and the SIMilarity Environmental Correction (SIMEC) over water (Sterckx et al., 2014). This study illustrates the performance of iCOR for Sentinel-2 over coastal, transitional and inland waters by comparing the image retrieved water-leaving reflectanceswith in-situ optical data collected during field campaigns in European lakes and AERONET-OC measurements. Specific attention is given to the impact of applying an adjacency correction for inland waters . Keywords: iCOR; OPERA; adjacency effects; SIMEC; inland, coastal and transitional waters; AERONET-OC; in-situ optical data; water-leaving reflectance References Sterckx, S., Knaeps, E., Adriaensen, S., Reusen, I., Keukelaere, L. De, Hunter, P., 2015. Opera : an Atmospheric Correction for Land and Water. Proc. Sentin. Sci. Work. 3–6. Sterckx, S., Knaeps, S., Kratzer, S., Ruddick, K., 2014. SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters. Remote Sens. Environ. doi:10.1016/j.rse.2014.06.017 Guanter, L., Del Carmen González‐Sanpedro, M., Moreno, J., 2007. A method for the atmospheric correction of ENVISAT/MERIS data over land targets. Int. J. Remote Sens. 28, 709–728. doi:10.1080/01431160600815525

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• 15:00 - ACOLITE processing of Sentinel-2 data: Evaluation and applications in coastal and inland waters
  Vanhellemont, Q.; Ruddick, K. - Natural Science (Belgium)

  Show Abstract

  With the advent of freely available data from the Landsat 8 (2013-...), Sentinel-2A (2015-...) and soon Sentinel-2B (2017-...) missions, uptake of high resolution water remote sensing has drastically increased in the coastal and inland water (hereafter "aquatic") community. No standard atmospheric correction and processor for aquatic applications was provided for any of these missions, so several algorithms were developed by different teams. In this presentation the ACOLITE atmospheric correction is discussed. ACOLITE is freely available and can process data from a number of sensors, including Landsat 5/7/8 and Sentinel-2. ACOLITE performs a two-step atmospheric correction: first, the Rayleigh reflectance is computed based on sun-sensor geometry and second, the aerosol reflectance over water is estimated using the SWIR bands and simply extrapolated to the visible and near-infrared bands in order to derive water-leaving reflectance. The performance of ACOLITE is evaluated using autonomous AERONET-OC measurements in Belgian coastal waters. Same-day imagery from L8 and S2 is compared in terms of water-leaving reflectance for the common band set. The impact of noise from the SWIR bands used in this approach is discussed. Processing challenges for inland waters are also highlighted, with proposed updates to the atmospheric correction algorithm. Novel aquatic applications are highlighted, such as monitoring of local sediment transport phenomena (turbid wakes) and events (dredging) using L8 and S2, and the detection of intense algal blooms with S2/MSI.

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• 15:20 - Sentinel-2/MSI ocean colour with the Polymer algorithm
  Steinmetz, François; Ramon, Didier - HYGEOS

  Show Abstract

  The Polymer algorithm is a robust and generic atmospheric correction method, designed to recover the ocean colour in presence of sun glint. It has been applied to multiple sensors, and is used within the Ocean Colour Climate Change Initiative project (OC-CCI). Initially developed for the open ocean, it has been extended to process case 2 waters, with the inherent advantage of not requiring negligible water reflectance in near infrared bands. The sun glint is a critical aspect of the Sentinel-2 MSI water observations: since the revisit of both S2A and S2B is done in the same viewing conditions, many areas are continuously affected by sun glint over a season, leading to long periods of no coverage if the sun glint is not recovered. The results of Sentinel-2 MSI processed with Polymer will be presented and evaluated.

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TEP - Exploitation Platforms

Chairs: Marin, Alessandro (Solenix.ch), Amler, Esther (ESA ESRIN)

08:30 - 10:30

• 08:30 - A New Platform for Global Urban Monitoring and Analysis - The Urban TEP
  Esch, Thomas (1); Asamer, Hubert (1); Balhar, Jakub (2); Boettcher, Martin (3); Boissier, Enguerran (4); Hirner, Andreas (1); Mathot, Emmanuel (4); Marconcini, Mattia (1); Metz, Annekatrin (1); Permana, Hans (3); Soukop, Tomas (2); Uereyen, Soner (1); Svaton, Vaclav (5); Zeidler, Julian (1) - 1: German Aerospace Center (DLR), Germany; 2: GISAT, Czech Republic; 3: Brockmann Consult, Germany; 4: Terradue, Italy; 5: IT4Innovations - Technical University of Ostrava, Czech Republic

  Show Abstract

  The upcoming suite of Sentinel satellites in combination with their free and open access data policy will open new perspectives for establishing a spatially and temporally detailed monitoring of the Earth’s surface. However, the capability to effectively and efficiently access, process, analyze and distribute the mass data streams from the Sentinels and high-level information products derived from them poses a key challenge. This is also true with respect to the necessity of flexibly adapting the processing and analysis procedures to new or changing user requirements and technical developments. Hence, the implementation of operational, modular and highly automated processing chains, embedded in powerful hard- and software environments and linked with effective distribution functionalities, is of central importance. This contribution introduces the TEP Urban platform that aims at the utilization of modern information technology functionalities and services to bridge the gap between the technology-driven EO sector and the information needs of environmental science, planning, and policy. Key components of the system are an open, web-based portal connected to distributed high-level computing infrastructures and providing key functionalities for i) high-performance data access and processing, ii) modular and generic state-of-the art pre-processing, analysis, and visualization, iii) customized development and sharing of algorithms, products and services, and iv) networking and communication. These services and functionalities are supposed to enable any interested user to easily exploit and generate thematic information on the status and development of the environment based on EO data and technologies. The TEP Urban platform is supposed to initiate a step change in the use of EO data by providing an open and participatory platform based on modern ICT technologies and services that enable any interested user to easily exploit and generate thematic information on the status and development of the built environment. So far more than 240 institutions from 41 countries have requested U-TEP data and system access.

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• 09:00 - Scalable EO Value Added Services (EO VAS) For Land Monitoring
  Kolitzus, David (1); Milcinski, Grega (2); Riffler, Michael (1) - 1: GeoVille, Austria; 2: Sinergise, Slovenia

  Show Abstract

  Nowadays most EO services are still based on downloading source data to the local environment, before performing basic preprocessing (e.g. compositing, mosaicking, atmospheric correction) and starting with specific analyses relevant for the service. Structured availability of data within a cloud environment makes it possible to automate tasks and run them faster. However, this work-process is still either too slow or the required computing power/bandwidth too high to be scalable to regional or even global scales. GeoVille and Sinergise have joined forces to solve this problem by connecting a standard scripting environment with Sentinel Hub's web services enabling innovative EO value added services (EO-VAS). EO-VAS will create a scalable on-demand service, which actually will generate and deliver EO products to users worldwide in a cost-effective manner. To demonstrate the approach, we have designed an initial service allowing for multi-temporal land classification in an extremely efficient manner. It takes spectral indices, such as for example the maximum NDVI pixel value, from a multi-temporal image stack with multiple observations and creates clusters based on the spatio-temporal variability of the index. This allows investigating seasonal vegetation variations and to discriminate various land cover types, such as cropland (high variation of vegetation throughout the year), bare soil (no variation, low vegetation index value) and grassland/forest (low variation, high vegetation index value). More detailed and accurate classifications are possible by introducing additional indices, derived metrics and region-specific thresholds. The Sentinel Hub web service is used to create the spectral indices for any chosen area and season. This comes as an input to a clustering procedure. As there is practically no need to store the data of intermediate steps, the service can be provided in real-time. When a request comes, it can be globally run without large IT resources, limited only by "regional EO knowhow" with regard to different spectral index patterns in various regions. A side effect of this simplicity is the given flexibility - it is possible to change the "rule engine" and parameters and see the result in a matter of seconds. This opens the possibility to develop and evolve services in a very fast and efficient manner. The main technological novelty lies in a cost-effective approach, which uses resources only when somebody is asking for results, thus making the services more affordable. The functional novelty is expressed through its connecting characteristics - connecting end-user communities with developers, who can build their services on top of the initial tool-set and commercializing them through the platform without the need for large investments in processing and storage infrastructure or data processing developments.

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• 09:15 - Playing with ESA Hydrology Exploitation Platform
  Martínez, Bernat; Romero, Laia - isardSAT, Spain

  Show Abstract

  ESA Hydrology Exploitation Platform (HEP) is a project aim at facilitating discover, access, process, visualisation and sharing of water information derived from Earth Observation data such as flood mapping, water level, water quality and the assimilation of these data into hydrological models. ESA HEP team will show the existing features and capabilities of this HEP through an on-line demonstration. ESA HEP services could be accessed through https://hydrology-tep.eo.esa.int/#!thematic.

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• 09:30 - Forestry TEP changes the paradigm in value adding of remotely sensed data
  Häme, Tuomas (1); Tergujeff, Renne (1); Rauste, Yrjö (1); Farquhar, Clive (2); van Zetten, Peter (2); Bennett, Victoria (3); deGroof, Arnaud (4); Hämäläinen, Jarno (5); van Bemmelen, Joost (6); Seifert, Frank Martin (6) - 1: VTT Technical Research Centre of Finland Ltd. (FI); 2: CGI (UK); 3: Science and Technology Facilities Council STFC (UK); 4: Spacebel (B); 5: Arbonaut Oy Ltd. (FI); 6: European Space Agency, ESRIN

  Show Abstract

  The Forestry Thematic Exploitation Platform (Forestry TEP) has recently entered the pre-operational phase. This means that the principal functionalities are in place and a higher number of users can start using it. At the time of writing of the abstract, the database of interested organizations has more than fifty members from academia, research organizations, private sector, public administration, and NGO’s. The user pool is growing when marketing of the platform is now intensifying. User interest in Forestry TEP has exceeded all the expectations. The enthusiasm clearly shows that such one-stop-shop for Earth Observation services in forestry has a strong demand in the user community. The users are seeing the benefits of working on the cloud instead of having to download and install data and software on their own computing facilities that may be incapable to process the vast volumes of Copernicus data. Presently six thematic processing services are available on the Forestry TEP - for computing vegetation indices and for mapping of land and forest cover, biomass and change. Through a voting system available on the Forestry TEP web site, a user can affect the prioritization of future applications. In addition, a user can utilize all the features of the Sentinel Application Platform SNAP, Monteverdi/Orfeo toolbox, and an open source Geographic Information System QGIS. The user can search the data on the Forestry TEP Platform, switch to access remotely any of the above-mentioned software tools, process data using these tools, save the result to the platform, and continue processing there or download the result to his own computer. Relevant images from the search can be saved to a Data Basket where they can be found also for the future processing in another session. The user can upload his own reference data to train the land cover or biomass models or he can generate the data interactively with QGIS on the platform, for instance. Additionally, via a web-based developer interface, it is possible for service providers to develop their existing or completely new applications to be run on the platform. Applications and result products can be shared to designated users or openly to everybody. In the operational phase, there will be a possibility also to sell products and offer application software as a service on the platform. Already now, data privacy and security functionality is in place. The platform presently offers access to Sentinel-1, Sentinel-2, and Landsat-8 data. More data types, including commercial data will be introduced according to user interest. Users can search over a geographic region not only for the available satellite images but also for existing products that have been computed and shared by other users. The Forestry TEP is being developed by VTT Technical Research Centre of Finland as the coordinator, application and user specialist, CGI IT UK as the system developer and integrator, Science and Technology Facilities Council (STFC UK) as the principal data access and infrastructure provider, and Spacebel (BE) and Arbonaut (FI) as application and service experts. In addition to using the Climate and Environmental Monitoring from Space (CEMS) facility and cooperative ground segment of STFC, Sentinel data can experimentally be downloaded from the Earth Observation Innovative Platform Testbed Poland (IPT) facility. It is expected that for the future operational phase to start in 2018, the Copernicus Data and Information Access Services (DIAS) will offer a potential infrastructure solution for Forestry TEP. https://forestry-tep.eo.esa.int/

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• 09:45 - The Polar thematic Exploitation Platform
  Fleming, Andrew (1); Puestow, Thomas (2) - 1: BAS, United Kingdom; 2: C-Core Canada

  Show Abstract

  The volume and variety of Earth Observation data available for the polar regions is growing rapidly, providing the opportunity for ever more complex investigations and exploitation in support of polar communities. The European Space Agency (ESA) and other satellite operators have been at the forefront of collecting, analysing, processing and disseminating new data and information from EO satellites. With this increase in volume of data and range of uses, there are new challenges in order to fully utilise and exploit this capacity. ESA has established a series of Thematic Exploitation Platforms (TEP) which will provide the necessary collaborative environment which will deliver the resources and capabilities required for users’ exploitation work. The TEP projects address present challenges and opportunities in scientific data exploitation and operational applications by collocating data, processing capabilities and ICT infrastructure, providing a complete cloud based work-environment for users. The Polar TEP addresses the requirements and challenges of the diverse polar user community. This presentation will describe the Polar TEP concept and the range of potential uses it will support. We will present details of the working environment where users can bring their algorithms, applications and development activities directly to the data. It will also cover the rich set of polar themed EO and complimentary datasets, relevant toolboxes and processing capabilities, plus functionality to allow deployment of user defined workflows and processing environments. The PTEP is currently in a pre-operations phase and will increasingly be used by real users. We will report on progress made in providing platform access to early users. We will also outline the ongoing pilot project which will demonstrate the potential of Polar TEP to investigate current and future iceberg risk in Baffin Bay. The pilot project will integrate a diverse set of data, processors and models to allow users to investigate linkages between iceberg trajectories, changes in ice sheet velocity, glacier calving rates and ocean circulation. The integration of these components and toolsets will allow Polar TEP users to easily investigate questions about changing iceberg populations in support of regional climate change studies, evaluate risk assessments and inform infrastructure and ship routing decisions.

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• 10:00 - Supporting Sustainable Food Production from Space
  Volden, Espen (1); Romeo, Antonio (2); Mougnaud, Philippe (1); Amler, Esther (1); Migdall, Silke (3); Muerth, Markus (3); Bach, Heike (3); De Avila Siqueira, Andreia (4); Colapicchioni, Andrea (4); Goor, Erwin (5); Gilliams, Sven (5); Van Roey, Tom (5); Dean, Andy (6); Suwala, Jason (6) - 1: ESA-ESRIN, Italy; 2: Rhea Group; 3: Vista-GEO, Germany; 4: CGI; 5: VITO, Belgium; 6: Hatfield, Canada

  Show Abstract

  In line with the paradigm shift in Earth Observation of “Bringing the users to the data”, ESA provides collaborative, virtual work environments giving access to EO data and tools, processors, and ICT resources through coherent interfaces. These coherent interfaces are categorized thematically, tailored to the related user communities and named Thematic Exploitation Platforms (TEP). The Food Security Thematic Exploitation Platform (FS-TEP) is the youngest out of seven TEPs and is developed in an agile mode in close coordination with its users. It will provide a “one stop platform” for the extraction of information from EO data for services in the food security sector mainly in Europe & Africa, allowing both access to EO data and processing of these data sets. Thereby it will foster smart, data-intensive agricultural and aquacultural applications in the scientific, private and public domain. The FS-TEP builds on a large and heterogeneous user community, spanning from application developers in agriculture to aquaculture, from small-scale farmers to agricultural industry, from public science to the finance and insurance sectors, from local and national administration to international agencies. To meet the requirements of these groups, the FS-TEP will provide different frontend interfaces. Service pilots will demonstrate the platform’s ability to support agriculture and aquaculture with tailored EO based information services. The project team developing the FS-TEP and implementing pilot services during a 30 months period (started in April 2017) is led by Vista GmbH, Germany, supported by CGI Italy, VITO, Belgium, and Hatfield Consultants, Canada. It is funded by ESA under contract number 4000120074/17/I-EF.

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• 10:15 - A Single Step from Software Prototype to Massive Processing
  Gilles, N. (1); Clerc, Sebastien (1); Aspetsberger, M. (1); Craciunescu, V. (1); Ceriola, G. (1); Campbell, Gordon (2); Leone, Rosemarie (2) - 1: ACRI-ST, France; 2: ESA, Esrin

  Show Abstract

  Today, massive processing resources are often reserved to a very limited number of users mostly because of the human investment needed to start using the resources. Users will generally be required to: · Specify their hardware requirements and reserve the resources · Learn the APIs to access the system · modify, package or sometimes rewrite their prototype code in order to make it compatible with the system This not only raises the entry bar to start using the system, but it also tends to lock forever the software to a specific platform. In the frame of the Coastal Thematic Exploitation Platform (CTEP), we have developed a radically different approach based on the idea that one should be able to integrate a software on the platform with a few clicks. The only constraint on the software being that all output files should be placed in a specific folder, the exact same code can be run on a stand-alone linux machine and on the CTEP cluster. This makes integration much simpler and facilitates maintenance of the software. Our solution relies on a number of specific software tools: · An original python-based implementation of a WPS server (WISPY). WISPY is managing a dynamical database of processing services which can be created, modified or deleted on-the-fly. · A processor integration mechanism and user interface. The interface allows users to define the WPS service (name, abstract, input parameters) and upload the software packaged as archive file. The CTEP automatically creates a container with the software and records in the WPS database. The interface also allows the user to share the processing service with other users. · Built-in pre- and post-processing steps to retrieve input files from the data archive, store processing results in the user data storage area, and register georeferenced images as layers for visualization. These automatic steps avoids the need to learn specific APIs. In addition, effort has been made to facilitate the development: · The CTEP provides interactive remote desktops based on noVNC for development and prototyping · A large range of pre-installed python libraries for python developers · Systematic recording of execution logs on the cluster for debugging · On-line access to SNAP and QGIS for in-depth analysis of Sentinel or geospatial data. We will describe the technical solution and report on the first experience of the system on the integration of processing services for coastal environment monitoring. As expected, simple software can be integrated on the platform in one hour typically (including debugging time), with little if any learning curve required.

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TEP - Exploitation Platforms

Chairs: Charalampopoulou, Vasiliki (Betty) (GEOSYSTEMS HELLAS S.A.), Volden, Espen (ESA-ESRIN)

11:15 - 13:15

• 11:15 - The SAFE Scientific Exploitation Platform
  Santoro, Francesca (1); Carbone, Marianna (1); Amoruso, Leonardo (1); Abbattista, Cristoforo (1); De Santis, Angelo (2) - 1: Planetek Italia s.r.l., Italy; 2: INGV – Istituto Nazionale di Geofisica e Vulcanologia

  Show Abstract

  Exploitation Platforms (EPs) have been becoming the preferred way to distribute EO data and derived information since their concept introduction in 2013. They have been declined with several approaches (e.g. Thematic, Mission EP…) according to the intended users community. A new one, specifically designed to satisfy the needs of the scientific community is described in this paper, demonstrating how EP concept perfectly fits and opens up to new possibilities. In fact, aside with the already assessed advantages this EP defines a set of thematic data coming from different domains, provides their easy centralized availability and provides algorithms and processing capabilities in order to test, tune and assess scientific outcomes. The study case is the SAFE (SwArm For Earthquake study) project, funded by the European Space Agency (ESA), in the framework of "STSE Swarm+Innovation”, and coordinated by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), that deals with the integrated analysis of several physical parameters whose abnormal variations have been found to be possibly associated with impending earthquakes (EQs). In the frame of the project Planetek has developed the so-called SAFE Exploitation Platform with the aim of sharing its outcomes and results, and of demonstrating the implemented techniques and scientific algorithms. Main purpose of the SAFE project is the investigation of the phase preceding large earthquakes with the aim to identify any electromagnetic signal eventually related to the forthcoming seismic events. In particular, the project has been intended to assess the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC), using both space-borne data coming from the ESA’s Swarm Constellation satellites together with ground geophysical data from different sources (e.g. in-situ instruments, seismic catalogues and other archives). The platform is specially designed to integrate both Swarm-satellites dataset with these ground-based geophysical data; it allows for accessing and visualizing data, performing online user-adjustable analyses and disseminating the project’s results to the scientific community. At a functional level, the SAFE Exploitation Platform provides means to properly configure and perform the collection and automatic update of data from external catalogue sources, to make them available to the user for browsing, visualization of their geographical information on the world map, and to perform a set of customized analyses, which can be configured according to the scientific findings and can then contribute to their assessment at a global scale. The SAFE Platform is able to integrate both engineered software (in C/C++) and prototypal implementations (in Matlab® and Python). Among the advantages we can mention: direct (and simplified) web access to data from different sources, to global results and to algorithms configuration and execution environment; unique reference deployment so to always share the latest version of the scientific algorithms, that have fast and flexible evolution maintaining a prototypal implementation approach. Finally it includes also integrated dissemination instruments, as a Web Portal intended to share the project’s results with the international scientific community, collect feedbacks on algorithms and results, and to highlight all the related events/ initiatives. This paper presents the scientific Exploitation Platform approach, with its architecture and functionalities, with a special focus on the SAFE case study in which its effectiveness has been demonstrated, with scientists and users involved in geophysical studies, for the multiple analyses in geomagnetic, ionospheric and seismic data domain and the contribution to the investigation of EQ preparation process.

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• 11:30 - The EO4Atlantic Pathfinder Regional Exploitation Platform
  McGlynn, Sinead (1); Juracic, Ana (1); O'Callaghan, Derek (1); Hanlon, Lorraine (1); McBreen, Sheila (1); Campbell, Gordon (2) - 1: Parameter Space, Ireland; 2: ESA/ESRIN

  Show Abstract

  A prototype platform for Earth Observation data in the Atlantic region (EO4Atlantic) is being developed in Ireland, and is funded by the European Space Agency. The project is led by Parameter Space Ltd., an Irish SME with expertise in platform development for a number of scientific missions, in collaboration with a number of SMEs including Treemetrics, Techworks Marine and iGeotec. Trial users are located in relevant institutions and organisations located along the Atlantic coastline, with an interest in satellite data. The EO4Atlantic platform will allow users to analyse large data sets in a high-performance capacity, without the need to download large data volumes to their computers. The users can make use of existing available software (e.g. open source tools currently being used for analysis of satellite or sensor data) or upload their own tools to the platform and run their analysis close to the data. The objective of the EO4Atlantic pathfinder platform is to support the development and delivery of EO based information services, based on high volume EO data access and processing, with a focus on expert and non-expert users of EO data from the European Atlantic region. The platform is designed to demonstrate how best to use and integrate existing capabilities and infrastructure in the Atlantic region, and how a full regional exploitation platform could operate in this area with potential new capabilities and infrastructure which will be implemented in the medium to long term. Customised toolkits are made available through the platform for testing. These include open source toolboxes for analysis of satellite data and workflows allowing the chaining together of different tools to create multiple processing steps. End to end services also provide products such as sea surface wind speed evaluations for renewable energy exploitation, cloud-free pixel databases for high-cloud areas, forestry monitoring and management tools, flood risk assessment in coastal regions, and ecological and physical observations of inland and coastal waters. These services are designed to complement the common issues and goals of the regional and European initiatives specific to the Atlantic area.

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• 11:45 - Satellite Agricultural Monitoring With Cloud Platforms
  Shelestov, Andrii (1); Kolotii, Andrii (1); Vasiliev, Vladimir (2); Lavreniuk, Mykola (1); Yailymov, Bohdan (1) - 1: Space Research Institute NAS Ukraine and SSA Ukraine; 2: EOS Data Analytics

  Show Abstract

  With launch of Sentinel-1 and Sentinel-2 missions new era of geospatial science in agricultural domain based on open data has started. High spatial (10 m) and temporal resolution (6 days of revisit period) leads to big amount of satellite data that couldn’t be processed within local computational infrastructure due to timely download, difficulties in data storage and processing. On the other hand such new quality of satellite imagery is very efficient for crop type mapping, area estimations etc [1-3]. These issues can be solved with use of modern satellite platforms for dealing with big amounts of geospatial data. Among them Google Earth Engine (GEE) and Amazon Elastic Compute Cloud (Amazon EC2) with Amazon Simple Storage Service (Amazon S3) are good examples of different ways of processing geospatial data in the cloud. GEE provides high-performance computation on free of charge basis with some amount limitations (in test mode only). With GEE geospatial analyst can use wide range of already developed methods for different modern tasks that occur in daily routines quite easy and very intensively. On the other side users are forced to use data catalog with already preprocessed wide range of satellite data. In case of SAR data this leads to some difficulties in further data exploitation. Within exploitation of Amazon EC2 and S3 services users are allowed to download and preprocess data in timely efficient way with use of scalable computational, networking and storage infrastructure. Amazon already hosts Sentinel-2 data for ESA (within official bucket) and Sentinel-1 for Alaska Space Facilities. Actually, Amazon Web Services and GEE demonstrate different ways to organization of cloud based geospatial data processing – more flexible with requirements of higher competence from end-users or restricted with easier daily exploitation. References 1. Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782. 2. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science, 5, 17. 3. Kussul, N. N., Lavreniuk, N. S., Shelestov, A. Y., Yailymov, B. Y., & Butko, I. N. (2016). Land Cover Changes Analysis Based on Deep Machine Learning Technique. Journal of Automation and Information Sciences, 48(5).

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• 12:00 - UrtheCast Data and Information Platform: How To Democratise Access To Earth Observation
  Ramos Perez, Jose Julio - Deimos Imaging SLU, an Urthecast company, Spain

  Show Abstract

  Urthecast's vision to democratise the access to Earth Observation touches ground with our data distribution and exploitation ecosystem, the UrthePlatform. This platform is putting together: - Data sources - an extensive and rich data sources offer (including our current operational satellites Deimos-1 and Deimos-2, public satellite data sets like Landsat and Sentinels, our partner satellites of the Pangeo Alliance, and the future UrtheDaily and OptiSAR constellations); - Geo-analytics - an ever-growing set of EO services and applications specifically designed for quick combination, analysis and extraction of information (including bring-your-own-algorithm capabilities); - Visualisation - complete portfolio of visually captivating presentation options (including graphs, 2D- and 3D-maps of both raster and vector layers); - GIS - close integration with unmatched Geographical Information Systems (GIS); - Marketplace - the possibility to monetise developed services and products; - Cloud - securely running over the world-biggest and most advanced cloud infrastructure; - Community - a massively big community of remote sensing experts, engineers and data scientists developing algorithms and final users consuming Earth Observation imagery and analytics; - Vertical integration - seamless compatibility with technologies used for companies in much larger industries (b2b and b2c model) and sectors like forestry, agriculture, infrastructures, urban planning, defence and intelligence. UrthePlatform provides unhindered and near universal access of EO imagery and data at affordable price point, in formats and on platforms that do not require expertise, within an eco-system that attracts third-party investment and innovation, that significantly broaden the utility of the data for organisations and individuals. This combination of data wealth, engineering effectiveness, expansion possibilities and massive community makes the UrthePlatform a perfect place for both EO and data scientists to deploy their work and make their work truly useful for their institutions and the society This presentation will introduce this UrthePlatform and discuss how its user could benefit from it.

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• 12:15 - Rationales For An Open Cloud Transition: The Case Of Bringing The EGI Federated Cloud As A Commodity For The Geohazards Scientific Community
  Rossi, Cesare; Caumont, Hervè; Pacini, Fabrizio - Terradue Srl, Italy

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  Earth observations from satellites produce vast amounts of data. In particular, the new Copernicus Sentinel missions are playing an increasingly important role as a reliable, high-quality and free open data source for scientific, public sector and commercial activities. Latest developments in Information and Communication Technology (ICT) facilitate the handling of such large volumes of data, and European initiatives (e.g. EOSC, DIAS) are flourishing to deliver on it. In this context, Terradue is moving forward an approach resolutely promoting an Open Cloud model of operations. With solutions to transfer EO processing algorithms to Cloud infrastructures, Terradue Cloud Platform is optimising the connectivity of data centres with integrated discovery and processing methods. This is for example the case with the Geohazards Exploitation Platform initiative, an R&D activity funded by ESA. Implementing a Hybrid Cloud model, and using Cloud APIs based on international standards, the Platform fulfils its growing user needs by leveraging capabilities of several Public Cloud providers. Operated according to an “Open Cloud” strategy, it involves partnerships complying with a set of best practices and guideline: • Open APIs. Embrace Cloud bursting APIs that can be easily plugged into the Platform’s codebase, so to expand the Platform offering with Providers offering complementary strategic advantages for different user communities. • Developer community. Support and nurture Cloud communities that collaborate on evolving open source technologies, including at the level of the Platform engineering team, when it comes to deliver modular extensions. • Self-service provisioning and management of resources. The Platform’s end-users are able to self-provision their required ICT resources and to work autonomously. • Users rights to move data as needed. By supporting distributed instances of its EO Data management layer, the Platform delivers the required level of data locality to ensure high performance processing with optimized costs, and guarantees that value added chains can be built on top of intermediate results. • Federated Cloud operations. The Platform’s collaborative environment and business processes support users to seamlessly deploy apps and data from a shared marketplace and across multiple cloud environments. As a recent case, thanks to the integration within the Platform of the Open Cloud Computing Interface (OCCI), and the close partnership between EGI and Terradue (a Service Level Agreement signed in September 2016 allowing Terradue to access a set of EGI federated Cloud providers in Europe), our provisioning of ICT resources supports ever more demanding exploitation scenarios. At this stage, EGI compute and storage resources from GOEGRID-GWGD (Germany) are used to support the ESA SNAP Sentinel-1 COherence and INtensity (COIN) Service, an on-demand processing model accessed by GEP users. Also, EGI compute and storage resources from ReCaS Bari (Italy) and BELNET-BEGRID (Belgium) are used to support the global scale systematic production of InSAR Browse Services integrated on GEP by the DLR. As Platform services, they automatically produce interferograms out of Copernicus Sentinel-1 acquisitions, over a subset of the global strain rate model, where volcanoes eruptions and earthquakes are most likely to impact society.

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• 12:30 - SPOT World Heritage: preserve and promote new enhanced SPOT 1-to-5 products
  Nosavan, Julien; Henry, Patrice; Hosford, Steven - CNES, France

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  SPOT 1-to-5 satellites have collected more than 15 million images all over the word during the last 30 years from 1986 to 2015 which represents a unique historical dataset. Spot World Heritage (SWH) is the CNES initiative to preserve and promote this SPOT archive by providing new enhanced products on an open Web platform. A first step has begun in 2015 with the start of the repatriation of the last SPOT data hosted in the Receiving Stations spread across the world in the CNES central archive system. Meanwhile, some preliminary SWH processing chains have been developed with the production of more than 100.000 orthorectified SPOT products, provided by CNES through the French Land products data Centre THEIA. From mid-2017, the SWH initiative will move into another phase with the development of operational SWH processing chains in line with Sentinel-2 “standards” to allow deeper time series analysis. The first one is based on the current SPOT operational processing chain and will provide SWH-L1A products which are first exploitable images with radiometric corrections. The following ones will provide SWH-L1B and SWH-L1C products, geometrically compatible with ESA Sentinel-2 products, SWH-L1C being the orthorectified product. SWH processing will take place on CNES High Performance Computing Centre to take advantage of SPOT archive proximity. Dedicated means are being put in place to process the whole SPOT archive using 24-core processors and optimized solutions for file sharing (GPFS), deployment (Docker) and cataloging (Elastic Stack). Finally, all the generated SPOT products will be accessible free of charge to registered users via the Web. First SWH products are expected to be distributed in 2018 while the whole archive is expected to be processed within 2 years until 2020, depending on the timing of SPOT data retrieval from the reception stations.

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• 12:45 - The Digital Transformation of Earth Observation Market - challenges and opportunities in moving from the “pipeline” to the “platform” business model
  Manieri, Andrea (1); Spito, Nicolò (1,2) - 1: Engineering Ingegneria Informatica SpA, Italy; 2: Politecnic of Turin

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  Recently, a great attention has been given to the exploitation of Earth Observation data, as a mean of industrial innovation and source of potential societal benefits. EU is at forefront in Earth Observation technologies: ESA launched the Sentinel Constellations, a set of redundant satellites that will offer high availability and resiliency as required by industries to run businesses. Several attempts and approaches have been experimented with alternate success in terms of self-sustainability, easy-of-use and scalability: from Thematic Exploitation Platforms , Business incubators to a Marketplace for EO services and data . All these approaches seem to enable a pipeline business model, i.e. where business is based on the acquisition of resources (a product and/or service) that are pushed to the consumer through the value chain in a unidirectional way. However, the Digital Transformation is radically changing the market landscape: ubiquitous connectivity, hands-held technology and user interactions are enabling elements of the platform business model, as successfully exemplified in various markets such as AirBnB, Uber, Google, etc. The platform model, instead, focuses on the creation of value through establishing an intelligent networking among users: where pipelines create value “on-top” of managed resources, platforms (that usually don’t even own such resources) create value by linking producer and consumer of resources. Platforms, as analysed in depth by S.P. Choudary (Choudary et.al. 2015), execute as a content aggregator that can simultaneously satisfy different type of interests. Platforms exploit also the phenomenon of network externalities, i.e. a service increases in value whenever increases the number of interacting individuals. Externalities could be of two types: same side (e.g. as in the telecommunication networks) or cross-side. Platform model exploits cross-network externalities, which are linked to the diffusion of the product, not among members on the same side of the market, but to the diffusion of the product on another network (or side). For example, Amazon marketplace bridges producers and consumers, while Uber helps drivers and riders to meet or AirBnB links housekeepers and tourists. These features also allow the business model to scale faster, thanks to the nature of the user, who could be both resource provider as well consumer (prosumer): this creates new opportunities, but also new challenges to face with. The breakthrough work of Choudary on the platform analysis models highlights the distinctive features of this new approach as well as the best practices that facilitate its understanding and implementation. This presentation aims to open a debate among stakeholders and to illustrate the initial hypothesis about how to implement the platform model in the EO sector for the benefit of all actors involved.

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• 13:00 - The Earth Observation Broker Platform for the Energy Sector
  Partington, Kim Charles (1); Lefort, Thomas (1); Debart, Carles (2); Reeve, Chris (3); Cetinic, Frano (4); Hartmann, Knut (5); Gasperi, Jerome (6) - 1: Geocento Limited; 2: Kongsberg Satellite Services; 3: Satellite Applications Catapult; 4: GlobeSAR; 5: Eomap Gmbh; 6: Jeobrowser

  Show Abstract

  The EO Broker platform is designed to encourage the uptake of earth observation by the energy sector by supporting users through stages of pre-procurement including product discovery, feasibility and supplier interaction. The open source platform has been developed through support from ESA's GSTP programme and as such includes some innovative brokering capabilities for products and services, but at the same time is aimed to support relatively easy uptake by both users and suppliers. The design has been influenced strongly by user cases derived from the energy sector, including from ESA's "EO4OG" projects, but has benefitted also from guidance by a project Steering Committee consisting of oil and gas industry professionals, The platform is at a mature stage of development, but is not operational. Its capabilities will be demonstrated through the presentation, along with an update on planned exploitation beyond the development phase of the project, and interested users and suppliers are invited to get in touch with the team.

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Data Cube

Chairs: Baumann, Peter (Jacobs University | rasdaman GmbH), Desnos, Yves-Louis (ESA-ESRIN)

14:15 - 15:45

• 14:15 - The Datacube Manifesto
  Baumann, Peter (1); Merticariu, Vlad (1); Misev, Dimitar (1); Hogan, Patrick (2) - 1: Jacobs University, Germany; 2: NASA, USA

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  Recently, the term datacube is receiving increasing attention as it has the potential of greatly simplifying "Big Earth Data" services for users by providing massive spatio-temporal data in an analysis-ready way. However, there is considerable confusion about the data and service model of such datacubes. With this Manifesto we provide a concise, vendor-neutral, standards-aware definition of datacubes, The six simple rules are based on our two decades of experience in datacube modeling, query languages, architectures, distributed processing, standards development, and active deployment of Petascale datacube services at some of the largest data centers worldwide. In particular, the intercontinental EarthServer initiative is demonstrating large-scale use of datacubes, with Petabyte portals at the time of the conference and experimental data center federation between Europe and Australia. Further, the authors are editors and main writers of geo datacube standards in OGC and ISO, as well as the generic datacube extension to ISQL SQL. We exemplify feasibility and advantages of datacube services based on large-scale running services and discuss seamless connection of servers and visual clients through open datacube standards. We hope that by providing this crisp guidance we can clarify discussion and support assessment of technologies and services in this exciting new technology field.

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• 14:45 - Copernicus Climate Data at your fingertips - ECMWF’s data archive as a data cube
  Wagemann, Julia (1); Kakaletris, George (2); Apostolopoulos, Konstantinos (2); Kouvarakis, Manos (2); Merticariu, Vlad (3); Siemen, Stephan (1); Baumann, Peter (3) - 1: European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom; 2: Communication and Information Technologies (CITE) S.A., Greece; 3: Jacobs University Bremen, Germany

  Show Abstract

  The Meteorological Archival and Retrieval System (MARS) is the world’s largest archive of meteorological and climate data. It currently holds more than 250 petabytes of operational and research data; and data of the three Copernicus services, Atmosphere Monitoring Service (CAMS), Climate Change Service (C3S) and Emergency Management Service (CEMS). ECMWF’s current data retrieval system is an efficient Download service for global fields and geographical subsets thereof. The system offers data in either GRIB or NetCDF format. The growing volume of the data, however, makes it challenging for users to fully exploit long time-series of climate reanalysis data (more than 35 years), as generally, more data than actually required has to be downloaded and then extensively processed on local machines. As part of the EarthServer-2 project, ECMWF’s MARS archive has been connected with an OGC-based standardized web service layer, in order to offer on-demand access to and server-based processing of ECMWF data for a non-meteorological audience. This connection transforms ECMWF’s data archive into a flexible data cube that allows for the efficient retrieval and processing of geographical subsets and individual point data information at the same time. Downloading ECMWF’s ERA-interim data is no longer required and data access can directly be integrated into custom processing workflows. The approach combines the efficient retrieval of data from MARS with a MARS request with intelligent data cube processing with rasdaman, an efficient array database technology. FeMME, a metadata management engine, is responsible to connect both processes and to send back the data to the user. The pilot allows users to request data via an OGC Web Coverage Processing Service (WCPS) request. The application then translates the WCS request into an appropriate MARS request to retrieve data from the archive. The returned data is on-the-fly registered with rasdaman and, after being processed as requested, returned to the user in a standardized way and a chosen format encoding (e.g. netCDF, CSV or JSON). Based on a practical use-case from the climate sciences, the presentation will showcase how open data from the Copernicus Climate Change Service from ECMWF’s MARS archive can directly be retrieved and processed with the help of a WC(P)S request. A specific focus will be set on the benefits data provider and data users gain from offering and accessing large volumes of Earth Science data as a data cube.

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• 15:00 - The Earth System Data Cube Service
  Brandt, Gunnar (1); Mahecha, Miguel (2); Fomferra, Norman (1); Permana, Hans (1); Gans, Fabian (2) - 1: Brockmann Consult GmbH, Geesthacht, Germany; 2: Max Planck Institute for Biogeochemistry, Jena, Germany

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  The ability to measure and quantify our environment, including ourselves, has increased significantly in recent years. Big data is now affecting almost every aspect of life and there are no signs that this trend is going to cease any time soon. For the natural environment, Earth Observation has been driving the data revolution by developing and putting into operation many new sensors with high spatial resolution and observation frequency generating high-quality products on a global scale to which everyone has free access. With this wealth of data on hand, the chances to describe and understand the complex dynamics of our planet and particularly the role of humans in affecting the Earth System are unprecedented. The bottleneck for many applications is, however, no longer the lack of suitable data, but the means to turn the huge amounts of available data from different sources into valuable information. Accessing several product archives, handling of big data volumes, providing adequate computing and network resources, and working with different product formats and data models are challenging even for experienced users. Furthermore, coping with these issues consumes lots of time, resources that are no longer available for the actual task of data exploitation and research. The Earth System Data Cube (ESDC) service aims at reducing such overhead for the joint exploration of relevant parameters of the coupled Biosphere-Atmosphere system. Currently, more than 30 parameters, most of them from Earth Observation and numerical models, are processed to share a common spatio-temporal grid and a common data model that greatly facilitate multivariate analysis. The service is provided through a virtual research environment based on the Jupyterhub technology, which offers access to the ESDC, cloud processing, and APIs for Python, Julia, and Python. There is a growing number of tailored open source software tools for typical operations that make working with the ESDC very efficient and enable collaboration between users. Moreover, an advanced visualisation application provides a graphical user interface for intuitive and interactive exploration of the ESDC. We present here different use cases that are currently implemented with user of the service. The examples, which comprise the calculation of marine primary productivity, biogeochemical model optimization, the development of a regional Ecological Observation System, the combination of social data on human disasters with extreme events in the ESDC, underpin the diversity of potential applications of the ESDC service. As the service matures on its way to operationality, we are welcoming new users to explore the potentials the ESDC for their own applications and to further advance the capabilities of the service with us.

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• 15:15 - Application Of The Data Cube Concept For Multi‐Temporal Satellite Imagery – A Complete Open Science Workflow For Data Science In EO
  Kristen, Harald (1); Jacob, Alexander (1); Vicente, Fernando (2); Marin, Carlo (1); Monsorno, Roberto (1); Notarnicola, Claudia (1) - 1: Eurac Research, Italy; 2: DARES Technology, Spain

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  ESA´s Sentinel satellites provide satellite imagery in an unprecedented temporal and spatial resolution, shared as open data to everyone. ESA foresees that its Earth Observation (EO) archive will grow from the 23 PB in 2018 to more than 51 TB in 2022, with the Copernicus Sentinel missions being the major driver. To exploit this large amount of data fully, the EO user community, both scientific and commercial, has to change the way it is using remote sensing imagery. It will not be feasible anymore to store and process EO data on single workstations, especially when it comes to time series analysis on larger scale. This paper shows a novel approach to big data science for EO, which is able to deal with the specific challenges of long time series processing using only Free and Open Source Software (FOSS). In detail, we will address the problem of land cover and vegetation mapping using time series of Sentinel-1 data, by exploiting the temporal evolution of the interferometric coherence. The analysis will be conducted for the study area of South Tyrol in Italy as part of the recently started ESA SEOM SlnCohMap project ( http://www.sincohmap.org ). All the exploited data is hosted and processed directly on the high performance cloud infrastructure of the Eurac Research Sentinel Alpine Observatory ( http://sao.eurac.edu ). In detail, the pre-processed data is organized in data cubes, implemented in the raster array database RASDMAN. This guarantees fast and standardized access to the data via the OGC Web Coverage Service (WCS), following the principle “you only get what you need”. Furthermore, a good portion of the classic processing tasks such as sub-setting, re-projection, resampling and time series aggregation are directly done in the data cubes via the OGC protocol WCPS (Web Coverage Processing Service), according to the second principle “bring the processing to the data”. Because of its simplicity, the open source policy and its rising popularity in the EO community, Python is used as programming language for the implementation of the land cover and vegetation mapping. In detail, to download and process data via WCPS, we developed a Python module that sends WCPS queries in the right format to the Rasdaman server and transforms the response in data structures that can be directly used for further analysis in Python. For the land cover and vegetation classification two well-described algorithms in this field, Random Forests and Support Vector Machine, are applied. All the Python scripts run on a Jupyter server, which again is running in the SAO cloud. GIT is used as version control system to document and publish the created code for potential users under the MIT license. This fosters collaboration as other researchers can clearly see, comment and help developing the code. With the proposed approach the EO users can process and analyze large amounts of data in a cloud infrastructure, accessing services and data remotely using a high level programming language. In this way, EO users can focus on algorithm development, instead of dealing with data preparation.

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• 15:30 - Evaluation of Array Database to Manage and Query Massive Sensor Data
  Joshi, Jigeeshu; Pebesma, Edzer; Appel, Marius - Institute für Geoinformatik (ifgi), Heisenbergstrasse 2, 48149, Münster, Germany

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  Many environmental monitoring observations are recorded at a fixed number of stations with constant temporal frequency. Arrays are a natural choice to represent these, with space and time as the two dimensions. Current implementations of Sensor Observation Services (SOS) from Open Geospatial Consortium (OGC) however typically use a normalized relational database as data backend. If data size grows to billions of records, querying and updating the database becomes relatively slow, with indexes taking up a lot of resources. This study evaluates the use of SciDB, an array database as a backend for massive time series data. In multi-dimensional array database system like SciDB dimensions are used as index. Indexes are implied and not stored, which saves substantial resources. Moreover, in the array data model, data that are close together are placed close together on the disk. This is advantageous for sensor observation data with spatial and temporal patterns. Thus, proximity in storage of correlated data improves efficiency in query processing. In a use case, SciDB features like parallelization, array dimension manipulation, chunk storage and compression are demonstrated by using an air quality dataset provided by the European Environment Agency. It is essentially a time series with air quality observations recorded by all member states at selected stations in Europe. The study compares SciDB as array database to PostgreSQL (which is a popular choice in geospatial community) as relational database. A common platform was set up to test the performance and compare the approaches of the two systems. Results show that SciDB has significant advantage for queries like sequential scan, group by aggregation, join, and computation of quantiles on the used dataset. An index on PostgreSQL table reduces the response time for filter and slicing queries, but the index size grows substantially as data size increases. On the other hand, the SciDB compression technique greatly reduces the disk storage space. The result of these tests and the discussions presented are particularly useful for Earth Observation studies that require multi-dimensional data management.

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Round Table TEP-DataCube-Citizen Science

16:15 - 16:35

• 16:15 - Round Table
  

  Show Abstract

  Exploitation Platforms/Citizen Science

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Lightning Talks

17:00 - 18:15

• 17:00 - EO-based Mapping of Environmental Health Risks: the H2020 EOXPOSURE Project
  Dell'Acqua, Fabio (1); De Vecchi, Daniele (1); Frery Orgambide, Alejandro C. (2); Gamba, Paolo (1); Lage, André (2); Marinoni, Andrea (1); Plaza, Antonio (3); Plaza, Javier (3); Scavuzzo, Marcelo (4,5); Shimoni, Michal (6); Lanfri, Mario Alberto (5) - 1: University of Pavia, Pavia, Italy; 2: Universidade Federal de Alagoas, Maceió, Brazil; 3: Universidad de Extremadura, Cáceres, Spain; 4: Universidad Nacional de Córdoba, Córdoba, Argentina; 5: Comisión Nacional de Actividades Espaciales, Córdoba, Argentina; 6: Ecole Royale Militaire - Koninklijke Militaire School, Brussels, Belgium

  Show Abstract

  The EU H2020 EOxposure project [1] started in March 2017. Its goal is to build tools to quantify the exposure of population and economic assets to multiple risks using novel information layers from current and future Earth Observation (EO) missions, including open data from Copernicus [2], as well as the growing sensor web on the ground. The project exploits the novel concept of the human exposome [3], i.e. the set of exposures to which an individual is subjected through his/her own existence. It includes the entire history of interactions with the environment, including air and water quality, food and exercises, as well as living habits and diseases that may spread. The cutting-edge fusion of this concept with EO and sensor data aims at measuring the human exposure to threats that are external to each individual, and quantify the interactions between human beings and the environment. By building open geospatial information tools upon data coming from multiple sources, at different spatial and temporal scales, the EOxposure project aims at providing free public information (“open”) services, enabling citizens to understand the threats to which they are exposed, and decision makers to take more informed and effective actions against them. Specifically, EOxposure will focus on threats connected to housing conditions, disease spread, as well as security and health issues in urban and peri-urban areas, where population is concentrated. The new tools will build upon the consortium expertise on nutrition- and vector-borne disease models, urban heat monitoring and material characterisation, satellite data processing, and geospatial data fusion, realising interdisciplinary working groups dedicated to the above-mentioned applications. To do so, EOxposure enlists institutions from Europe and South America, merging expertise on exposure to risk in both developed and developing countries. The full paper will report more details on the project content and projected goals, and will present its future development plans. References [1] Tools for Mapping Human Exposure to Risky Environmental conditions by means of Ground and Earth Observation Data (EOXPOSURE). A EU H2020 project. Web site at http://www.h2020-eoxposure.eu/ [2] The EU Copernicus initiative. Online at http://copernicus.eu/ [3] C.P. Wild, “The exposome, from concept to utility,” Int. Journal of Epidemiology, vol. 41, pp. 24-32, 2012.

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• 17:03 - WebAssembly, How This New Web Standard For In Browser Processing Can Help EO Data Valorization
  Decoster, Nicolas (1); Nosavan, Julien (2) - 1: Magellium, France; 2: CNES, France

  Show Abstract

  JavaScript, as a dynamic language, can be slow. Some processings are simply too huge to be run in the browser. And even if processing time would be OK in JavaScript, most processings don’t have implementation in JavaScript, and being able to use them in the browser means rewriting the code. For this reasons, for now, this kind of processings have to be executed server side, which could have an impact on user experience. Well, in fact, until WebAssembly. WebAssembly (or wasm) is a new Web standard that is useable now and which is supported by all major browsers’ vendors. Wasm is a low-level binary format that is not meant to be written by hand but that is a compilation target. One can see it as a kind of bytecode or assembly language. It lives alongside JavaScript and complements it in terms of processing powers (wasm can have near native performance). Moreover wasm as a compilation target allows us to use existing processings written in other languages (mainly C/C++ for now, more to come) in the browser. So WebAssembly is a new technology that opens new doors for Web architectures. There are lots of scenarios where it can be used. One has limited processing server but its users are OK to host some processings? One needs to do some complex real-time processing for some interactive data visualization? Some users don’t want to upload some of their confidential data on some processing server? One needs a bit more power for a mobile version of a Web site or a Web app? One has an existing image processing algorithm, but it is written in C and wants to use it client side? Etc. WebAssembly might help on these cases. And of course, Earth Observation and its data with great variety of usages and kinds of processing can greatly benefit from WebAssembly. In this talk, we will show what WebAssembly is, how it integrates in Web architecture, how one can use existing C/C++ code to target WebAssembly for use in the browser and how to use WebAssembly for EO data valorization, using an illustrating proof of concept that integrates raw data management (i.e. not served as a classic image server for example), its visualization and some existing or new processings, all in the browser.

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• 17:06 - Standardized Access and Processing of Earth Observation Data within a Regional Data Middleware
  Eberle, Jonas; Truckenbrodt, John; Schmullius, Christiane - Friedrich Schiller University Jena, Germany

  Show Abstract

  Increasing archives of global satellite data present a new challenge to handle multi-source satellite data in a user-friendly way. Any user is confronted with different data formats and data access services. In addition the handling of time-series data is complex as an automated processing and execution of data processing steps is needed to supply the user with the desired product for a specific area of interest. In order to simplify the access to data archives of various satellite missions and to facilitate the subsequent processing, a regional data and processing middleware has been developed. The aim of this system is to provide standardized and web-based interfaces to multi-source time-series data for individual regions on Earth. For further use and analysis uniform data formats and data access services are provided. Interfaces to data archives of the sensor MODIS (NASA) as well as the satellites Landsat (USGS) and Sentinel (ESA) has been integrated in the middleware. Also the OGC services from the Sentinel-Hub are currently being tested for a simpler data access. Various scientific algorithms, such as the calculation of trends and breakpoints of time-series data, can be carried out on the preprocessed data. Jupyter Notebooks are linked to the data and further processing can be conducted directly on the server using Python and the statistical language R. Standardized web services as specified by OGC are provided for all tools of the middleware. Currently, the use of cloud services is being researched to bring algorithms to the data.

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• 17:09 - The DASE "Standardized Open Dataset" by the IEEE GRSS: an open test bench for classification and target detection algorithms
  Dell'Acqua, Fabio (1,2); Iannelli, Gianni Cristian (1,2); Kerekes, John (3); Moser, Gabriele (4); Pierce, Leland (5); Goldoni, Emanuele (6) - 1: Ticinum Aerospace s.r.l. - Pavia, Italy; 2: University of Pavia, Italy; 3: Rochester Institute of Technology, Rochester NY, USA; 4: University of Genoa, Genoa, Italy,; 5: University of Michigan, Ann Arbor MI, USA; 6: IT consultant, Mantova, Italy

  Show Abstract

  Scientific advances in classification and target detection on Earth Observation (EO) data are difficult to quantitatively assess against each other in the absence of a common dataset onto which their results can be evaluated. In order to ensure homogeneity in performance assessment of algorithms for information extraction that are proposed in literature, standardized remotely sensed datasets are particularly useful and welcome. As a contribution towards implementing fair comparison, the IEEE [1] Geoscience and Remote Sensing Society (GRSS) [2] and especially its Image Analysis and Data Fusion Technical Committee (IADF), has been organizing the Data Fusion Contest (DFC) [3] for some years now. On every new edition of the DFC one more specific open dataset is made available to the scientific community at large; contestant scientists and researchers can download it and use it to test their freshly developed algorithms. A consistent test dataset for all participating groups makes it possible to consistently assess results and makes it legitimate to rank them. After the contest deadline, the user who scored the highest is proclaimed as the winner. The “technical backing” of this effort is the so-called Data and Algorithm Standard Evaluation (DASE) website [4]. DASE can distribute to registered users a limited set of possible “standard” open datasets, together with some ground truth info, and automatically assess the processing results provided by the users. In this paper, we report on the birth of this initiative and present some recently introduced features. References [1] The Institute of Electrical and Electronics Engineers (IEEE) official website. Online at http://www.ieee.org/ [2] The IEEE Geoscience and Remote Sensing Society official website. Online at https://www.grss-ieee.org/ [3] D. Tuia, G. Moser, B. Le Saux, B. Bechtel and L. See, "2017 IEEE GRSS Data Fusion Contest: Open Data for Global Multimodal Land Use Classification [Technical Committees]," in IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 1, pp. 70-73, March 2017. doi: 10.1109/MGRS.2016.2645380 [4] IEEE GRSS Data and Algorithm Standard Evaluation (DASE) website. Online at http://dase.ticinumaerospace.com/

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• 17:12 - Automatic Processing of Sentinel data for Forestry Management in Guatemala
  Marti, Paula; Brillatz, Chloe; Petit, David; Costantini, Fabiano - Deimos Space UK, United Kingdom

  Show Abstract

  The forest in Guatemala covered a total of 3,711,366 hectares in 2012, which is a 34% of the country. The illegal exploitation of the forest environment is a real concern to the Guatemalan government. The government has made efforts to tackle this problem by embracing digital technologies, improving its processes and by pooling information between all stakeholder agencies. The FMAP (Forestry Management and Protection) project is part of the International Partnership Programme and its aim is to support the Guatemalan agencies by providing remote sensing data and derived information. The FMAP project is led by Astrosat and the project partners include Telespazzio Vega, EO Inc and Deimos Space UK. The Guatemalan partner institutions for FMAP are the National Forestry Institute (INAB), the National Council of Protected Areas (CONAP) and the Ministry of Agriculture (MAGA) amongst others. INAB and CONAP run currently a set of programmes to promote the recovery, restoration, management and production of the Guatemalan forests. These programmes offer monetary incentives to people to reforest or exploit the forest whenever it is done following a management plan that has been submitted and approved. The incentive programme has been very successful, resulting on more than 30,000 areas being registered in the system. These areas are spread all over the country and it is a challenge for CONAP and INAB to send field workers to check that they meet all necessary requirements for the incentive they are claiming. The work presented here shows how the KORE platform developed by Deimos is able to process and deliver automatically and constantly to the end users satellite imagery from the Sentinels and derived products such as vegetation indices, chlorophyll content, forest cover and changes in coverage for the areas they monitor. These products are used to highlight changes and to trigger warnings to INAB and CONAP when the conditions for the incentive are not met in an area or when the area is not used as reported. In addition, the KORE platform is also used to deliver data that allows monitoring illegal logging, which usually happens in remote areas and areas of difficult access.

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• 17:15 - A Collection Of Data Analytics For Earth Observation Time Series Analysis
  Filipponi, Federico; Valentini, Emiliana; Taramelli, Andrea - ISPRA, Italy

  Show Abstract

  Extended time series of Earth Observation products are increasingly providing consistent information to support downstreaming services in a wide variety of application domains and disciplines. The establishment of the Copernicus European Programme, served by specifically designed Sentinel satellites, activated the generation of large harmonized spatio-temporal datasets, freely available to users under six thematic services for the analysis of spatial features and temporal patterns as well as the monitoring of changes and anomalies. In last decades climate modeling employed many mathematical and statistical methodologies to extract concise information from large spatio-temporal datasets. More recently the availability of extended Earth Observation time series took advantage from the data analytics developed for climate modeling to analyse spatial features and temporal patterns. Despite there is a demanding need for data analytics to extract information from large EO product datasets, few open tools are collecting available techniques to handle raster time series. In addition, many techniques for spatio-temporal analysis can not deal with incomplete time series and require appropriate gap-filling methodologies to interpolate raster time series. We present the newly developed 'rtsa' (Raster Time Series Analysis) package for R programming language providing a collection of analytics to perform spatio-temporal analysis from raster time series. The package 'rsta' acts as a front-end to already available functions in various R packages, specifically designed to handle geographic datasets provided as raster time series. The available functions within the package allow the direct input of raster time series to extract concise and comprehensive information taking advantages of techniques like Empirical Orthogonal Function, Empirical Orthogonal Teleconnections, Self Organized Maps, Seasonal Trend Decomposition using Loess, Breaks For Additive Season and Trend, X-13-ARIMA seasonal adjustment. Since some techniques for spatio-temporal analysis can not deal with incomplete raster time series, a selection of gap-filling methods like DINEOF, spatio-temporal gapfill, linear and spline interpolation are incorporated in the package in order to interpolate missing values when required by both user and technique. Memory usage is optimized by the adoption of raster masks and the parallel processing support using multiple CPUs is available for some of the analysis techniques. The 'rtsa' package is available to R users in a free and open-source software.

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• 17:18 - Application of Time-continuous Approximation of Vegetation Activity Indicators Obtained from Multispectral Satellite Imagery to Monitoring of Lower Volga Arid Wetlands
  Kozlov, Alexander (1); Kozlova, Maria (2); Gorelits, Olga (2); Zemlianov, Igor (2) - 1: Lomonosov Moscow State University, Russian Federation; 2: N.N. Zubov State Oceanographic Institute, Russian Federation

  Show Abstract

  The key features of vegetation activity in arid wetlands are its high temporal dynamics, considerable spatial heterogeneity and extreme sensitivity to the environmental changes. These are the reasons why application of conventional techniques based on remote sensing data analysis to ecological monitoring of arid wetlands becomes challenging and can even produce misleading results. The lower Volga arid wetlands play very important role in local economy, being almost the only source of fresh water and the only region of naturally moistened soil to be used for agriculture and livestock among the surrounding desert and dry steppe. The vegetation activity proved to be the primary indicator of its state, that can be observed directly using satellite multispectral imagery. Because of the substantially big area of the region under study (over 9000 square kilometers), field observations cannot go on continuously all over its territory, and thus cannot provide certainly unbiased and representative results. The only source of information for balanced and broad view is remotely sensed data combined with meteorological, hydrological, geobotanical and other in-situ data. In this study we focus on indicators derived from the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series, a quantity that is recognized as one of Essential Climate Variables (ECV). It is developed for multiple satellite sensors and thus pretends to be universal for past and future global studies. Due to the above-mentioned exceptional features inherent to arid wetlands, FAPAR time series obtained from satellite imagery of a particular territory needs to be specially processed in order to produce more informative results. We intend to report a technique of constructing a time-continuous approximation of FAPAR time series along with several quantitative indicators for vegetation activity and its seasonal and annual dynamics. These indicators appear to reflect intrinsic properties of various plant communities of arid wetlands in lower Volga region, which cannot be derived from FAPAR time series directly. Our approach showed a great potential to be used in the analysis of ecosystem state. Examples of its application and comparison to the field data include plant cover classification, comprehensive ecosystem state estimation and quantitative mapping of key vegetation activity parameters for its monitoring. Once first published, the methodology undergoes continuous development and validation. New results on its application are going to be presented.

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• 17:21 - Sea ice monitoring system based on satellite data to support safe navigation in Arctic Seas
  Korosov, Anton; Babiker, Mohamed; Park, Jeong-Won; W. Hansen, Morten - Nansen Environmental and Remote Sensing Center, Norway

  Show Abstract

  A sea ice monitoring system is developed to support safe operations and navigation in Arctic Seas. The system is capitalized on the Geo-Scientific Platform as a Service (GeoSPaaS) developed at NERSC for aggregation of satellite, modeling and in situ data. It exploits Synthetic Aperture Radar (SAR) data from satellites as a major component of this system. Sentinel-1 data is delivered every day in near realtime for monitoring of sea ice and other environmental parameters. The system is based on the algorithms for (1) sea ice classification of different ice types and open water; (2) ice drift with sufficient resolution to map mesoscale ice motion and deformation fields; and (3) iceberg detection by combined use of SAR and high-resolution optical images. Furthermore, the system integrates SAR data with AIS data (Automatic Identification System) from vessels operating in sea ice areas. With AIS positions combined with SAR images it will be possible for ship captains to find sailing routes through open leads or thin ice and avoid areas ice with ridges and other difficult ice situations. Key user groups for the system include be shipping companies, oil and gas companies, operational met-ocean services, coastal and ship traffic authorities, risk management, and environmental organizations working in the Arctic. The system is developed under project SONARC, supported by the Research Council of Norway (proj. Number 243608).

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• 17:24 - Scalable Extraction of Small Woody Features (SWF) at the Pan-European Scale using Open Source Solutions
  Faucqueur, Loïc (1); Merciol, François (2); Damodaran, Bharatt Bhashan (2); Rémy, Pierre-Yves (1); Desclée, Baudouin (1); Dazin, Fabrice (1); Lefèvre, Sébastien (2); Sannier, Christophe (1) - 1: Systèmes d'Information à Référence Spatiale (SIRS), France; 2: IRISA (UBS Site) Campus de Tohannic BP 573 56 017 Vannes Cédex, France

  Show Abstract

  The Mapping of Small Woody Features (SWF) is to be included as part of a new high resolution layer covering the whole of Europe from Iceland to Turkey as part of the Copernicus pan-European component of the land monitoring service. Small Woody Features (SWF) represent some of the most stable vegetated linear and small landscape features providing numerous ecological and socio-cultural functions related to (i) soil and water conservation, (ii) climate protection and adaptation, (iii) biological diversity and (iv) cultural identity. By definition, SWF are small in width and spatial extent. Very High Spatial Resolution (VHSR) image data such as the available VHR_Image_2015 dataset from the European Space Agency (ESA) Copernicus Space Component Data Access (CSCDA) is required to detect their presence. Traditionally, mapping of linear features is done using Computer Aided Photo-Interpretation (CAPI) techniques, but existing automated approaches are not suitable for SWF mainly due to their very small characteristics. Furthermore, the mapping of SWF over such a large area (almost 6 million km²) requires a completely new automated approach to feature extraction that is capable to deal with (i) the large amount of data (greater than 100TB), (ii) the large number of individual image scenes (close to 25,000), (iii) the diversity of the European landscapes and (iv) process these data in a timely manner whilst ensuring a satisfactory degree of precision. A new scalable solution relying on open source components was developed to fulfil these requirements. In a first step, feature extraction is achieved through a novel, efficient implementation of differential attribute profiles (DAP) that are among the state-of-the-art image descriptors in remote sensing. In a second step, SWF are extracted in a semi-supervised context using an open source implementation of the popular Random Forests classifier. Besides demonstrating the strength of open source software for helping the large-scale production of land cover maps, we also introduce several new developments related to the DAP features. Those features are straightforwardly extracted from a prior tree-based, multiscale representation of the image. They allow to gather both spectral and spatial information in an efficient manner. Our proposal leads to a significant reduction of both computation time and memory footprints w.r.t. available codes, thus making possible to use such features at a very large scale under strong operational constraints (i.e. processing a Gigapixels image in a few minutes). The new algorithm is being implemented as part of the SWF HR Layer cloud computing based production chain and is currently under integration as an open source component of the Orfeo Tool Box (OTB) software suite.

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• 17:27 - Tool For SARAL/AltiKa Waveform Clustering
  Dutta, Suvajit (1,3); Ghosh, Surajit (1,2); Thakur, Praveen Kumar (1); Bhattacharyya, Soumya (2) - 1: Indian Institute of Remote Sensing, ISRO, India; 2: National Institute of Technology Durgapur, India; 3: Vellore Institute of Technology, India

  Show Abstract

  This article describes a classification tool for classify the SARAL/AltiKa waveforms. The tool was made by Python scripting language. Radar altimetry systems (like SARAL/AltiKa) determine the distance from the satellite to a target surface by measuring the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. This tool helps to cluster the altimetry waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism (Ghosh et al., 2016). The tool presented here is divided into two parts one part does the data indexing classification part, and one part is for developing the interface for ease of interaction with the user. For interface designing Tkinter package is being used, Tkinter is Python's de-facto standard GUI package. For algorithm calculation parts numpy-MKL package has been used, NumPy is the fundamental package for scientific computing with Python. From the tool user have to select the specific folder which contains the waveforms, then another destination folder has to select to store the waveforms after applying EMIF algorithm on it.The idea is to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform which will be further used for clustering. The tool is simple and easy to interact with the users, and it also takes very low disk space. Ghosh, S., Thakur, P., Dutta, S., Sharma, R., Nandy, S., Garg, V., Aggarwal, S., Bhattacharyya, S., 2016. A new method for SARAL/AltiKa Waveform Classification: contextual analysis over the Maithon reservoir, Jharkhand, India. In SPIE Asia-Pacific Remote Sensing International Society for Optics and Photonics. 98780G-98780G. doi:10.1117/12.2223777.

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• 17:30 - Fusing Machine Learning and Earth Observation for Sustainable Development in Dar es Salaam
  Iliffe, Mark (1); Anderson, Edward (2) - 1: University of Nottingham, United Kingdom; 2: World Bank, Tanzania

  Show Abstract

  As one of the fastest growing cities in Africa, Dar es Salaam is growing fast, adding more than 35,000 people to the city’s population each month. With over 70% of the city being unplanned and living in unplanned settlements there is a substantial risk to critical infrastructure, public health, clean water provision, and social stability. Floor Space Index (FSI) is one such tool that can assist governments in urban planning and development. The ratio of a building’s total floor area to the size of the piece of land upon which it is built, FSI offers critical insights into urban density, zoning, service provision, taxation, and indicators of change over time. In Dar es Salaam, the World Bank is leveraging the revolution in high-cadence geospatial imagery to develop new analytical capacity to deliver frequent and consistent FSI mapping across urban areas – creating a practical tool to assist government agencies in urban growth and development decisions. Starting in early 2017, we have utilised a stream of Earth Observation streams, from commercial imagery products from Planet to freely available open data from Sentinel, to understand how FSI can be achieved, then updated in a timely fashion. This approach has used novel machine learning techniques, such as Convolutional Neural Networks to automate FSI extraction, this has been alongside a manual survey of FSI, that has verified and validated the approach of this work. As such, this project derives benefits from both the datasets as outputs but also the process of developing new algorithmic and machine learning techniques. This paper presents these outputs and lessons learned from this project, discusses the impact of this work and presents conclusions on the relative merits of the spatial datasets involved for integrating stakeholders in understanding these complex outputs.

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• 17:33 - Sepal - System for Earth Observation Data Access, Processing and Analysis for Land Monitoring
  Lindquist, Erik; D'Annunzio, Remi; Finegold, Yelena; Fontanarossa, Roberto; Fox, Julian; Ortmann, Antonia; Togna, Cosimo; Vollrath, Andreas; Wiell, Daniel - UN-FAO, Italy

  Show Abstract

  The Forestry Department of the Food and Agriculture Organization of the UN introduces the System for Earth Observation Data Access, Processing and Analysis for Land Monitoring or SEPAL. SEPAL is a web-based platform for fast access and processing of different remotely sensed data sources designed to assist national forest monitoring and reporting for REDD+. It is addressing the existing challenges countries face when developing such monitoring systems due to difficulties accessing and processing remotely sensed data. Users are able to quickly process large amounts of data without requiring high network bandwidth or the need to invest in high-performance computing infrastructure. It further features an intuitive graphical user interface that enables also non remote sensing experts and novices to exploit earth observation data. As a result, SEPAL will help countries establishing and maintaining a Satellite Land Monitoring System (SLMS) capable of producing the information required to make consequential decisions about forest and land management. SEPAL includes different components that address a wide range of aspects with regard to forest and land monitoring. Automated pre-processing routines of freely available, high resolution optical (i.e. Landsat, Sentinel-2) and Synthetic Aperture Radar (SAR) data (i.e. Sentinel-1, ALOS K&C mosaics) allows for the rapid creation of nationwide, analysis-ready data sets. Those can be complemented by sample-based training and validation data generated via the Collect Earth Online module using very-high resolution imagery from various data providers. Modules for image segmentation, change detection, time-series analysis, data fusion and classification enables the user to derive relevant value-added products of high-quality. From the derived maps, area estimations on land use and land cover (change) are calculated by using a dedicated module that provides the user with the relevant statistics for the subsequent reporting process. The presentation will give an overview of the functionality of the platform, including some relevant examples of different use cases.

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• 17:36 - ESA BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes
  Ambrózio, Américo (1); Restano, Marco (2); Benveniste, Jérôme (3) - 1: DEIMOS/ESRIN, Italy; 2: SERCO/ESRIN, Italy; 3: ESA-ESRIN

  Show Abstract

  The scope of this work is to showcase ESA BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes. BRAT is a collection of tools designed to facilitate the processing of radar altimetry data from previous and current altimetry missions, including the upcoming Sentinel-3A L1 and L2 products. A tutorial is included providing plenty of use cases. BRAT's last iteration (4.1.0) was released in April 2017. Based on the community feedback, the frontend has been further improved and simplified whereas the capability to use BRAT in conjunction with MATLAB/IDL or C/C++/Python/Fortran, allowing users to obtain desired data bypassing the data-formatting hassle, remains unchanged. Several kinds of computations can be done within BRAT involving the combination of data fields, that can be saved for future uses, either by using embedded formulas including those from oceanographic altimetry, or by implementing ad-hoc Python modules created by users to meet their needs. BRAT can also be used to quickly visualise data, or to translate data into other formats, e.g. from NetCDF to raster images. GUT is a compilation of tools for the use and the analysis of GOCE gravity field models. It facilitates using, viewing and post-processing GOCE L2 data and allows gravity field data, in conjunction and consistently with any other auxiliary data set, to be pre-processed by beginners in gravity field processing, for oceanographic and hydrologic as well as for solid earth applications at both regional and global scales. Hence, GUT facilitates the extensive use of data acquired during GRACE and GOCE missions. In the current 3.1 version, GUT has been outfitted with a graphical user interface allowing users to visually program data processing workflows. Further enhancements aiming at facilitating the use of gradients, the anisotropic diffusive filtering, and the computation of Bouguer and isostatic gravity anomalies have been introduced. Packaged with GUT is also GUT's VCM (Variance-Covariance Matrix) tool for analysing GOCE's variance-covariance matrices. Both toolboxes are still being actively supported and developed by their respective consortiums, towards fulfilling the scientific community needs and increasing their didactic potential. BRAT and GUT toolboxes can be freely downloaded, along with ancillary material, at https://earth.esa.int/brat and https://earth.esa.int/gut.

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• 17:39 - Automated Processing Chains Via Python And Esa Snap Toolbox For Earth Observation: Two Applications For Sentinel-2 And Sentinel-3 Data Products
  Vecoli, Antonio - Tech For Space, Italy

  Show Abstract

  SNAP is the ESA application platform that supports the scientific exploitation of the Sentinels Earth Observation Missions by providing a common architecture for the toolboxes of Sentinel-1 , Sentinel-2 and Sentinel-3. The SNAP developers provided a specific Python module called Snappy that give access to the SNAP API, developed in JAVA, directly in Python. This approach can lead to the definition of automated Python processing chains that improves the exploitation of the Sentinels data products with the interaction between SNAP and the Python scientific computing packages. Here two examples of these SNAP - Python applications will be introduced in the digital poster, focusing on Sentinel-2 and Sentinel-3 data products. The Sentinel-2 MSI product will be used to implement a Python-SNAP processing chain including the atmospheric correction (Sen2cor processor) and two simple band ratio algorithms ,that can provide computationally fast and easy to use parameters. These indexes came out to be in good correlation with water quality parameters for lake waters (CHL, CDOM,DOC). The Sentinel-3 OLCI data product will be processed with an automated Python processing chain that can compute the TOA reflectance of each band from the TOA spectral radiances provided by OLCI, and then the true color image of the scene will be obtained after the implementation of a contrast enhancing technique available in the Python scientific packages. The automated Python processing chains provide important advantages for the general community of SNAP users and for the development of the ESA toolbox for Earth Observation itself. First of all the noticeable flexibility of the Python programming language will be used to work directly on the Sentinels data product, avoiding the interaction with the desktop version of the SNAP toolbox. But the most important point is that the processing chains in Python could be introduced to implement new scientific algorithms or plugins, that are called SNAP operators, in the original SNAP toolbox. So each user could be at the same time also an independent developer providing new enhancements and extensions to the official ESA SNAP toolbox with an open source approach

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• 17:42 - SAR Altimetry Processing On Demand Service for CryoSat-2 and Sentinel-3 at ESA G-POD
  Benveniste, Jérôme (1); Dinardo, Salvatore (2); Sabatino, Giovanni (3); Restano, Marco (4); Ambrózio, Américo (5) - 1: ESA.ESRIN, Italy; 2: He Space/EUMETSAT; 3: Progressive Systems/ESRIN; 4: SERCO/ESRIN; 5: DEIMOS/ESRIN

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  The scope of this presentation is to feature the ESA-ESRIN G-POD SARvatore service to users for the exploitation of the CryoSat-2 data, which was designed and developed by the Altimetry Team at ESA-ESRIN EOP-SER (Earth Observation – Exploitation, Research and Development). The G-POD service coined SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) is a web platform that allows any scientist to process on-line, on-demand and with user-selectable configuration CryoSat-2 SAR/SARin data, from L1a (FBR) data products up to SAR/SARin Level-2 geophysical data products. The Processor takes advantage of the G-POD (Grid Processing On Demand) distributed computing platform to timely deliver custom-processed data products and to interface with ESA-ESRIN FBR data archive (155’000 SAR passes and 41’000 SARin passes - 118 TB of CryoSat data storage). The output data products are generated in standard NetCDF format (using CF Convention), therefore being compatible with the Multi-Mission Radar Altimetry Toolbox (BRAT) and other NetCDF tools. By using the G-POD graphical interface, it is straightforward to select a geographical area of interest within the time-frame related to the Cryosat-2 SAR/SARin FBR data products availability in the service catalogue. The processor prototype is versatile allowing users to customize and to adapt the processing, according to their specific requirements by setting a list of configurable options (which can be augmented upon user request). After the task submission, users can follow, in real time, the status of the processing. From the web interface, users can choose to generate experimental SAR data products as Stack data and Range Integrated Power (RIP) waveforms. The processing service, initially developed to support the development contracts awarded by confronting the deliverables to ESA’s, is now made available to the worldwide SAR Altimetry Community for research & development experiments, for on-site demonstrations/training in training courses and workshops, for cross-comparison to third party products (e.g. CLS/CNES CPP or ESA SAR COP data products), and for the preparation of the Exploitation of the Sentinel-3 Surface Topography Mission, by producing data and graphics for publications, etc. Initially, the processing was designed and uniquely optimized for open ocean studies. It was based on the SAMOSA model developed for Sentinel-3 Ground Segment using CryoSat data. However, since June 2015, a new retracker (SAMOSA+) is offered within the service as a dedicated retracker for coastal zone, inland water and sea-ice/ice-sheet. In view of the Sentinel-3 data exploitation, a new flavor of the service has been initiated, exclusively dedicated to the processing of Sentinel-3 mission data products. The scope of this new service is to maximize the exploitation of the upcoming Sentinel-3 Surface Topography Mission’s data over all surfaces. Moreover, since June 2016, the high resolution EIGEN6C4 geoid based on GOCE DIR5 data is available in output products. The service is open, free of charge (supported by the SEOM Programme Element) for worldwide scientific applications and available at https://gpod.eo.esa.int/services/CRYOSAT_SAR/ More info can be read at: http://wiki.services.eoportal.org/tiki-index.php?page=GPOD+CryoSat-2+SARvatore+Software+Prototype+User+Manual

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• 17:45 - Copernicus Big Data Explotation For A Global Glaciers Monitoring Service
  Nascetti, Andrea; Di Tullio, Marco; Emanuelli, Nico; Nocchi, Flavia; Camplani, Andrea; Crespi, Mattia - Geodesy and Geomatics Division ­ DICEA ­ University of Rome La Sapienza, Rome, Italy

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  The glaciers are a natural global resource and one of the principal climate change indicator at global and local scale, being influenced by temperature and snow precipitation changes. Among the parameters used for glacier monitoring, the glaciers surface velocity is an important element, since it influences the events connected to glaciers changes (mass balance, hydro balance, glaciers stability, landscape erosion). The surface glacier velocity can be measured using both in-situ survey and remote sensing techniques. Although the in-situ surveys are accurate and have the advantage of allowing ice-flow monitoring at a high temporal resolution, it is difficult to cover wide and not accessible areas. On the other hand, satellite imagery enable the continuous monitoring of wide areas and provide information independent from logistic constraints. SAR data with respect to the optical one has several advantages: SAR imagery is characterized by very precise satellite orbit that provides high resolution and accurate mapping capabilities; moreover, SAR sensor has the remarkable advantage to collect images in any illumination and weather conditions. Thanks to Copernicus program, Sentinel-1imagery are available under a free access policy with very short revisit time (down to 6 days with the launch of the Sentinel-1B satellite) and the high amplitude resolution (up to 5 m), supplying huge amount of data for spatial and temporal studies. Therefore, it is necessary to change the processing way from the standard procedure ‘bring data to users’ to the opposite ‘bring users to data’ moving towards the Big Data paradigm also for the analysis of satellite and geospatial data. As a matter of fact, the users can directly upload algorithms to the dedicated infrastructure removing the required time for data transfer and allowing the development of innovative applications. The leading idea of this work is to continuously retrieve glaciers surface velocity using Sentinel-1 SAR amplitude data and exploiting the potentialities of the Google Earth Engine (GEE). GEE has been recently released by Google as ‘a platform for petabyte-scale scientific analysis and visualization of geospatial datasets’. It is a computing platform which can be used to run geospatial analysis using a dedicated High Performance Computing infrastructure, enabling researchers to access geospatial information and satellite imagery for global and large scale remote sensing applications. The archive includes more than thirty years of historical imagery and scientific datasets, daily updated and expanded; overall, GEE contains over two petabytes of geospatial data instantly available for analysis. The algorithm of SAR off-set tracking developed at the Geodesy and Geomatics Division of the University of Rome “La Sapienza” has been integrated in a cloud based platform that automatically processes large stacks of Sentinel-1 data to retrieve glacier surface velocity filed time series. Several results related to relevant glaciers (i.e. Baltoro (Karakoram), San Rafael and San Quintin (Chile), Aletsch (Switzerland)) also validated with respect already available and renown software (i.e. ESA SNAP, CIAS) highlight the potential of the Big Data analysis to automatically monitor glacier surface velocity fields at global scale, exploiting the synergy between GEE and Sentinel-1 imagery for implementing a global monitoring service.

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• 17:48 - A way to monitor and share thunderstorms from satellite data using the StormTrek app
  Michele, de Rosa (1); Picchiani, Matteo (1); Del Frate, Fabio (2); Sist, Massimiliano (2) - 1: GEO-K srl, Italy; 2: Tor Vergata University, Italy

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  The number of extreme weather events is increasing during the latest years as result of the climate changes in-progress. Novel applications of weather satellite data may provide an unevaluable support to the risk mitigation and the damages reduction phases in addressing extreme events occurrence. StormTrek is a mobile app, available on the Android platforms, which can detect, track and predict the behaviour of convective cells up to 30 minutes ahead. It is based on the output of the StormTrack algorithm which analyses the Meteosat Second Generation images in near real time every 15 minutes, or every 5 minutes over Europe with the rapid update service. The thunderstorms are displayed on a geolocalized map together other information like the cloud top temperature, the convective cell severity, the cloud direction, the cloud area and the very short term forecasts (nowcasting). These data are integrated over different layers with other Meteosat RGB products provided by the Eumetsat Web Map Service to provide a complete characterization of the weather situation to the final user. The latter can interact with the app’s interface sending a feedback about the nowcasting accuracy. This methodology has been prototyped to develop a further step of validation for the StormTrack algorithm, adopting a citizen science approach. In this way the user is not only stimulated to use the application, but he is invited to contribute to the development. At present, the app covers Europe, Africa, South America, Middle East and India and it's actively used by users mainly from South Africa, India and Eastern Europe. The increasing number of users from different countries has pushed the development of a new multilingual interface for app. Furthermore, in South Africa a virtual community was created to support the project.

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• 17:51 - Foss4G DATE: an open resource for DSMs generation from optical and sar satellite imagery
  Di Rita, Martina; Nascetti, Andrea; Crespi, Mattia - La Sapienza, Italy

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  One of the most important application of remote sensing is the generation of Digital Surface Models (DSMs), that have a large relevance in many engineering, environmental, Earth sciences, safety and security applications. The fully automatic generation of DSMs is still an open research issue. To this aim, the software DATE was developed to process both optical and SAR satellite imagery. DATE is a FOSS4G, conceived as an OSSIM (Open Source Software Image Map) plug-in, whose development started in summer 2014 in the framework of 2014 Google Summer of Code. The implemented tool is based on a hybrid procedure, whereby photogrammetric and computer vision algorithms are mixed in order to take the best of both. As regards the dense matching algorithm, for example, the Semi Global Matching as implemented in the OpenCV library is exploited. A special attention was paid in finding a solution for the epipolar resampling, which is not straightforward in case of optical and SAR satellite imagery due to their multiple projection centers; an original and computationally efficient approach was defined and implemented, introducing the GrEI (Ground quasi-Epipolar Imagery). In this work, the results obtained with DATE on some optical and SAR datasets are presented and assessed. As regards optical imagery, the ISPRS Working Group 4 Commission I on “Geometric and Radiometric Modelling of Optical Spaceborne Sensors”, provides a benchmark dataset with several stereo data sets from space borne stereo sensors; Worldview-1 and Cartosat-1 datasets were used. The accuracy in terms of NMAD ranges from 1 to 3 m for Wordview-1, and from 4 to 6 m for Cartosat-1. The results obtained show a general better 3D reconstruction for Worldview-1 DSMs with respect to Cartosat-1, and a different completeness level for the three analysed tiles, characterized by different slopes and land cover. As far as concerns SAR imagery, a dataset composed by two SAR stacks (one ascending and one descending) of three TerraSAR-X images each have been analysed. The accuracy evaluation has been carried out comparing the DSMs extracted to a more accurate reference DSM obtained with LiDAR technology. Results from the ascending and the descending stacks have been evaluated: quite better results, with RMSE even less than 6 m and almost no bias, are achieved with the descending stack, while the ascending stack shows a RMSE of about 7.5 m and a high bias, not really compliant with the absolute geolocation accuracy of the zero-Doppler SAR model. Instead, exploiting all the available DSMs in the merged DSM, global accuracy around 6 m is achieved, with a much higher completeness. These results are compliant with the accuracy required in several applications such as canopy volume and density estimation, urban volumes computation, emergency mapping. Furthermore, as far as some active and passive high resolution data are freely available (e.g. the Sentinel constellation), a massive processing of such data could contribute to the generation of a high resolution open source DSM, allowing, in the meantime, thanks to the revisiting time of the satellite, to produce also multitemporal analysis able to detect ground changes.

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• 17:54 - Fostering Open Source Software Resources for Geospatial Data Exploitation in Europe
  Pradhan, Chetan (1); Ilie, Codrina Maria (2); Tabasco, Antonio (3) - 1: EARSC, Belgium; 2: Terrasigna, Romania; 3: GMV, Spain

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  Open Source Software is no longer a novelty in the realm of geospatial data exploitation. The time when these solutions were regarded with a sceptical and hesitant eye have faded away. Today, in the private sector new business models have evolved, developing new links with open source; whilst in the R&D and academic environment, the requirement of building products and services using open source has become the status quo. There are many stable Open Source Solutions in the Geospatial Information domain, used all over the world. These solutions vary from desktop to server-side, from libraries to web tools. However, the open source environment is of great complexity, and this presents a barrier to much more widespread adoption, and the building of communities around different solutions. In this context, EARSC has started an initiative to understand the Open Source Solutions available for the Geospatial environment, to map out the different governance and community engagement models in existence, and to explore how to promote and sustain further developments on all facets, from technical to legislative. One particular challenge is that a lot of what is published as open source is developed in various R&D projects, most commonly as a response to an explicit request by the funding entity. Such solutions fail to develop a community that would drive the project beyond its initial resources. And that is the pivotal point that eventually determines whether an open source solution will be successful or not. The EARSC initiative aims to explore whether it is possible to create an environment that sustains such solutions: an environment that would not allow the resources invested in solutions to be lost once they no longer have the support that drove their initial development. In this presentation, EARSC will describe the activities of its Open Source Initiative working group, which has been analysing the rationale for a more coordinated method of managing the open source tools and components being developed for EO data exploitation, and defining the associated governance needed to foster a vibrant and active community of contributors and ensure the long-term sustainability of the initiative with benefit to the whole community including industry, institutional and research.

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• 17:57 - CATE – Access to and Analysis of Climate data by the ESA CCI Toolbox
  Fomferra, N. (1); Zühlke, M. (1); Gailis, J. (2); Bernat, C. (3); Hollmann, R. (4); Corlyon, A. (3); Pechorro, E. (5) - 1: Brockmann Consult, Germany; 2: S&T, Norway; 3: Telespazio Vega, UK; 4: Deutscher Wetterdienst, Germany; 5: ESA- ECSAT, UK

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  The ESA Climate Change Initiative has the objective to realise the full potential of the long term global EO archives that ESA together with its member states have established over the last 30 years, as a significant and timely contribution to the ECV databases required by the UNFCCC. As part of this programme ESA is making available to its climate data users a dedicated toolbox supporting the analysis of data across the various ECVs. Currently ESA ECV comprise 14 climate variables (aerosols, cloud, fire, greenhouse gases, glaciers, ice sheets, land cover, ocean colour, ozone, sea ice, sea level, sea surface temperature, soil moisture). This toolbox is called CATE = Climate Analysis Tooling Environment. The main objective of CATE is to equip climate users with the means to operate on CCI ECV data, overcoming three cardinal challenges: 1. Limited means to ingest ECV data spanning different ECV types into a common data model. 2. Limited means to apply algorithms homogeneously across data associated with different ECV types, 3. Limited means to conveniently analyse and visualise data drawn from 1. and 2. The CCI toolbox CATE provides easy access to all ESA CCI data available from the CCI Open Data Portal easy access to any climate data available through OPEnDAP easy access to local data in netCDF format deployable in the cloud through backend - frontend architectural design programming interface in scientific python 3 user interfaces: API (programming in scientific python), CLI (scripting), GUI (App)python backend desktop app based on Web Technologies as Frontend (Electron, React/Redux, TypeScript, Cesium Globe, OpenLayers)easiest integration of new operations in both CLI and GUI by adding annotated Python functionsintegrated workflow management workspace-based GUItime series analysisThe CCI Toolbox is targeting at a broad user community. Users of CCI data span a variety of communities, from ECV data scientific users to high-level policy drivers and decision makers. Moreover, the user community includes education and the knowledgeable public. Consultation with all users of CCI data is important to establish the detailed requirements of the toolbox and to review the different development stages of the toolbox.In this presentation we will highlight the user requirements and the corresponding main concepts of CATE, give a demonstration of the software and its usage from CATE Desktop and via the API, and stress the specifies of global climate time series data. We will encourage the users of the workshop to test CATE, to critically reflect on our software solution, and thus to foster the interactions between users and developers.

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• 18:00 - Serving earth observation data with GeoServer: addressing real
  Giannecchini, Simone; Aime, Andrea - GeoSolutions, Italy

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  The presentation will cover GeoSolutions experience in setting up GeoServer based production systems providing access to earth observation products, with indications of technical challenges, solutions, and deployment suggestion. The presentation will cover topics such as setting up a single unified mosaic from all the available data sources, tailoring access to it to different users, determining the most appropriate stacking order, dealing with multiresolution, different coordinate systems, multiband data, SAR integration, searching for the most appropriate products using a mix of WFS, CSW and so on, serving imagery with high performance WMS and WMTS, performing small and large data extractions with WCS and WPS, closing up with deployment examples and suggestions. The presentation will also cover latest developments like the development of the OpenSearch for EO extension that allows to expose EO Collection and products via OpenSearch, the enhancements to ImageMosaic for improved management of EO Time Series as well as the improved REST Interface for administering EO collections and products. Challenges for preprocessing EO data like Sentinel and Landsat will also be introduced.

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• 18:03 - Exploring New Capabilities for Global Ocean Colour Innovative Applications
  Isaac, Dura - HeraSpace, Spain

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  “Exploring New Capabilities for Global Ocean Colour innovative applications” Isaac Dura1 More than 10 years ago, ESA kicked off the Technology Transfer Programme Office (TTPO). Their mission is to inspire, and facilitate the use of space technology, systems and know-how for non-space applications. ESA has been very active, promoting this program in the entrepreneurial scene, for example the 26th of November 2016, ESA sponsored the Junction hackaton in Helsinki, where there were more than 1400 competitors. ESA offered, one of the two main prizes to the best idea for the Arctic, the application HeraSpace. HeraSpace proposed an app for optimising ocean fishing standards and best practices. Combining Copernicus satellite data with actual fishing data, fishing routes and selection could eventually be drastically improved. Particularly interesting were the features aimed at supporting sustainable exploitation of Arctic resources. ESA link from the Junction event. The HeraSpace team is composed for an international and highly experienced team, which is adding high doses of innovation, putting together a hype tech stack conformed, by real time data from the EUMETSAT Sentinel 3 Marine Copernicus satellite from ESA, and the Blockchain which warranties that the data performs in an unhackable system. The HeraSpace algorithm, uses data from the Sentinel 3 satellite launched by ESA in 2014, particularly interesting is the use of the OLCI, in order to analyze the existence of chlorophyll. The data retrieved from the Sentinel 3 satellite, delivers a very high quality real time data, contemplating variables like temperature, salinity, deep of the waters, pollution and levels of O2. This data is crossed, with data from an expert knowledge DB (seafood domain), the preferences of the user seafood company, and of course, another input is coming from the DB of the different legal regulations of each region. From the dynamical & real time intersection of all the mentioned inputs, HeraSpace builds the best possible route, in order to increase the company revenue, avoid administrative fines, and warranty the sustainability of the raw material (seafood). The space tech stack is served by the Blockchain, making sure that the optimal routes can´t be hacked (copied) by pirates, and it warranties in front of the administration that the company, complies with the current legal regulations. This is due to the unhackable Blockchain connection, built by HeraSpace between the administration, and the seafood enterprises. The toolbox for the Sentinel-3 satellite optical mission, supporting OLCI and SLSTR is of critical importance for HeraSpace. Like the parallel developments for Sentinel 1 and 2, the Sentinel 3 Toolbox is based on an evolution of the BEAM development platform. This common platform is called SNAP – SentiNel Application Platform. With the use of the Copernicus data access, his graphical user interface and the Open Data Protocol (ODATA). HeraSpace uses two dedicated Application Program Interfaces (API) for accessing the EO data stored in the file downloaded, using the OData protocol which accepts REST web services. OData Service Root URI for the CODA Web Service https://coda.eumetsat.int/odata/v1 CODA Web Service Resource Paths: /Products /Collections Query Options admitted by the CODA Web Service: $format: Specifies the HTTP response format of the record e.g. XML or JSON; $filter: Specifies an expression or function that must evaluate to true for a record to be returned in the collection; $orderby: Determines what values are used to order a collection of records; $select: Specifies a subset of properties to return; $skip: Sets the number of records to skip before it retrieves records in a collection; $top: Determines the maximum number of records to return; The default response format is Atom[RFC4287], a XML-based document format that describes Collections of related information known as “feeds”. The resulting products from the query can be filtered by ingestionDate, evictionDate, and also by UUID (Unique User Identifier). They are served to the user in MD5 downloads, that can be checked using the CODA Checksum function, in order to confirm the quality of the data with respect to the original one. Once HeraSpace will be deployed in the ESA cloud servers (Red Hat Enterprise Linux), HeraSpace will moderate the seafood industry, helping to measure the captures, boosting sustainability, and maintaining a healthy ocean ecosystem. 1. Email: isaacdura@heraspace.com Address: OsterBrooksWeg 14 b, Schenefeld, Hamburg. Germany. EUMETSAT EUM/OPS-SEN3/MAN/16/880763 v2 Draft, 24 January 2017 http://www.esa.int/spaceinimages/Images/2016/11/Junction_ESA_Arctic_Special_Prize_winner_HeraSpace

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Exhibitions - TEP Demo - Social Event

18:15 - 19:00

• 18:15 - Exhibitions - TEP Demos 3
  

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  Exhibitions - TEP Demos 3

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