Conference Agenda

For more than 20 years, “Earth Observation” (EO) satellites developed or operated by ESA have provided a wealth of data. In the coming years, the Sentinel missions, along with the Copernicus Contributing Missions as well as Earth Explorers and other, Third Party missions will provide routine monitoring of our environment at the global scale, thereby delivering an unprecedented amount of data.

As for global land monitoring and mapping the fleet of heritage and operational missions allow to analyse, extract, condense and derive relevant information on the status and change of our land cover, heading towards the development of a sustainable operational system for land cover classifications to meet the various user’s needs for land monitoring purposes.

ESA as either owner or operator has been and is handling a variety of heritage and operational missions such as Sentinel-2, Sentinel-3 on behalf of the European Commission, the latter based on 10 years MERIS heritage. Furthermore Proba-V is delivering since more than 3 years data to a growing land user base, again following on 15 years SPOT VGT heritage as operated by CNES and with data dissemination via Belgium including VITO as the current archive manager. Through missions such as Landsat, Earthnet builds on a heritage on land monitoring since >35 years.

While the availability of the growing volume of environmental data from space represents a unique opportunity for science and applications, it also poses a major challenge to achieve its full potential in terms of data exploitation.

In this context ESA has started in 2014 the EO Exploitation Platforms (EPs) initiative, a set of R&D activities that in the first phase (up to 2017) aims to create an ecosystem of interconnected Thematic Exploitation Platforms (TEPs) on European footing, addressing a variety of thematic areas.

The PROBA-V Mission Exploitation Platform (MEP), complements the PROBA-V user segment by offering an operational Exploitation Platform on the PROBA-V data, correlative data and derived products. The MEP PROBA-V addresses a broad vegetation user community with the final aim to ease and increase the use of PROBA-V data by any user. The data offering consists of the complete archive from SPOT-VEGETATION, PROBA-V , as well as selected high-resolution data/products in the land domain.

Together with the European Comission, ESA is furthermore preparing the way for a new era in Earth Observation, with a concept to bring users to the data, under the ‘EO Innovation Europe’ responding to paradigm shift in the exploitation of Earth Observation data.

The Copernicus Data Information and Access Service (DIAS) will focus on appropriate tools, concepts and processes that allow combining the Copernicus data and information with other, non-Earth Observation data sources to derive novel applications and services. It is foreseen that the data distribution and access initiatives will support, enable and complement the overall user and market uptake strategy for Copernicus.

The presentation will debrief on the current status and planning with regard to exploitation platforms in operations and planned with ESA involvement, as relevant for Land monitoring and land cover classification.

Sentinel-2 data are being distributed for more than a year now. However, they are still not as widely used as they should be based on their usefulness. The reason probably lies in technical complexity of using S-2 data, especially if one wants to use full potential of multi-temporal and multi-spectral imaging. Vast volume of data to download, store and process is technically too challenging.
We will present a Copernicus Award [1] winning service for archiving, processing and distribution of Sentinel data, Sentinel Hub [2]. It makes it easy for anyone to tap into global Sentinel archive and exploit its rich multi-sensor data to observe changes in the land. We will demonstrate, how one is able not just observing imagery all over the world but also creating its own statistical analysis in a matter of seconds, performing comparison of different sensors through various time segments. The result can be immediately observed in any GIS tool or exported as a raster file for post-processing. All of these actions can be performed on a full, worldwide, S-2 archive (multi-temporal and multi-spectral). To demonstrate the technology, we created a simple web application, called "Sentinel Playground" [3], which makes it possible to query Sentinel-2 data anywhere in the world.
Sentinel-2 data are only as useful as the applications built on top of it. We would like people to not bother too much with basic processing and storing of data but rather to focus on value added services. This is why we are looking for partners, who would bring their remote sensing expertise and create interesting new services.
[1] http://www.copernicus-masters.com/index.php?anzeige=press-2016-03.html
[2] http://www.sentinel-hub.com
[3] http://apps.sentinel-hub.com/sentinel-playground/

Accurate, detailed and continuously updated information on land cover is fundamental to fulfil the information requirements of new environmental legislation, directives and reporting obligations, to address sustainable development and land resource management and for supporting climate change impact and mitigation studies. Each of these applications has different data requirements in terms of spatial detail, thematic content, topicality, accuracy, and frequency of updates. To date, such demands were largely covered through bespoken services based on a variety of relevant EO satellite sensors and customized, semi-automated processing steps.

To address public and industry multi-user requirements, we present a system for retrieval of high resolution global land cover monitoring information, designed along Space 4.0 standards. The highly innovative framework provides flexible options to automatically retrieve land cover based on multi-temporal data streams from the Sentinel 1, Sentinel 2 as well as third party missions. Users can specify desired land cover data for any place on the globe for any given time period since the operational start of Sentinel-2, and receives a quality controlled output within hours or days (depending on product level).

The core of the operational mapping system is a modular chain consisting of sequential components for operational data access, pre-processing, time-series image analysis and classification, pre-acquired in-situ data supported calibration and validation, and service components related to product ordering and delivery. Based on the user’s selection for a target area, date/period, and the type of the requested product, the system modules are automatically configured into a processing chain tailored to sector-specific information needs.

The data access component retrieves all necessary data by connecting the processing system to Sentinel data archives (e.g. the Austrian EODC) as well as other online image and in-situ databases. After pre-processing, all satellite data streams are converged into data cubes hosting the time-series data in a scalable, tile-based system. Targeted land cover information is extracted in a class specific manner in the thematic image analysis, classification and monitoring module, representing the core of the processing engine. Key to the retrieval of thematic land cover data is an automated, iterative training sample extraction approach based on data from existing regional and global land cover products and in-situ data bases. The system is self-learning and -improving and thereby continuously building a global database of spatially and temporally consistent training samples for calibration and validation. Finally, the class specific land cover maps are assembled into a coherent land cover database according to the user’s specifications.

The developed system is currently tested in various R&D as well as operational customer projects. The aim is to solidify the performance of the various modules with a multi-staged opening of the system portal, starting with selected industry customers along B2B service models.

We will demonstrate the service capacity for a number of use cases, which are already applied for the current production of the High Resolution Layers within the Copernicus Land Monitoring Service, GlobalWetland-Africa, the Land Information System Austria (CadastrENV) and related mapping services for the international development sectors.

One of the main interests in exploiting Earth Observation (EO) data and collateral information in the Space and Security domain is related to the detection of changes on the Earth Surface; to this aim, having an accurate and precise information on Land Cover is essential. Thus it is crucial to improve the capability to access and analyse the growing amount of data produced with high velocity by a variety of EO and other sources as the current scenario presents an invaluable occasion to have a constant monitoring of Land Cover changes.

The increasing amount of heterogeneous data imposes different approaches and solutions to exploit such huge and complex datasets; the new paradigms are changing the traditional approach where the data are downloaded to users’ machines, and the key role of technologies such as Big Data and Cloud Computing are emerging as important enablers for productivity and better services, where the “processes are brought to the data”.

The European Union Satellite Centre (SatCen) is currently outlining a system using Big Data and Cloud Computing solutions, built on the results of two Horizon 2020 projects: BigDataEurope (Integrating Big Data, software & communities for addressing Europe’s Societal Challenges) and EVER-EST (European Virtual Environment for Research – Earth Science Themes). Main aims are: to simplify the access to satellite data (e.g. Sentinel missions); to increase the efficiency of the processing using distributed computing (e.g. by open source toolboxes); to detect and visualise changes potentially related to Land Cover variations; to integrate the final output with collateral information.

Through a web-based Graphical User Interface the user can define an Area of Interest (AoI) and a specific time range for the analysis. The system is directly connected to relevant catalogues (e.g. the Sentinels Data Hub) and the data (e.g. Sentinel-1) can be accessed and selected for the processing. Several SNAP operators (e.g. subset, calibration and terrain correction) have been chained, so the user can automatically trigger the pre-processing chain and the successive change detection algorithm (based on an in-house tool). The output is then mapped as clustered changes on the specific AoI.

The integration of the detected changes with Land Cover information and collateral data (e.g. from social media and news) allows to characterize and validate changes in order to provide decision-makers with clear and useful information.