Earth Observation data access through the Copernicus data distributor systems has paved the way to monitor changes on Earth, using Sentinel data. One of the main objectives of EOPEN is to fuse Sentinel data with multiple, heterogeneous and big data sources, to improve the monitoring capabilities of the future EO downstream sector. Additionally, the involvement of mature ICT solutions in the Earth Observation sector shall address major challenges in effectively handling and disseminating Copernicus-related information to the wider user community, beyond the EU borders. To achieve the aforementioned goals, EOPEN will fuse Copernicus big data content with other observations from non-EO data, such as weather, environmental and social media information, aiming at interactive, real-time and user-friendly visualisations and decisions from early warning notifications. The fusion is also done at the semantic level, to provide reasoning mechanisms and interoperable solutions, through the semantic linking of information. Processing of large streams of data is based on open-source and scalable algorithms in change detection, event detection, data clustering, which are built on High Performance Computing infrastructures. Alongside this enhanced data fusion, a new innovative, overarching Joint Decision & Information Governance architecture will be combined with the technical solution to assist decision making and visual analytics in EOPEN. Apart from EO product-oriented data management activities, EOPEN also exploits user-oriented feedback, tagging, tracking of interactions with other EOPEN users. EOPEN will be demonstrated in real use case scenarios in flood risk monitoring, food security and climate change monitoring.

The project addresses the security and the resilience of EU Ground Segments of Space Systems, meeting the crosscutting and the sectorial criteria of the EU critical infrastructures (2008/14). The Copernicus era has created a new market with the massive amounts of satellite data that the ground segments of space systems receive serve to the market and governmental bodies. A physical/cyber-attack to their installations or communication networks, respectively, would cause debilitating impact on public safety and security of EU citizens and public authorities. A physical attack on a space ground segment makes the distribution of satellite data problematic and, on the other hand, a cyber-attack in its data storage, access and exchange affects not only the reliability of space data, but also their FAIR standards: findability, accessibility, interoperability and reusability. Current approaches do not fully exploit the recent advances in surveillance mechanisms with robotic technologies and AI. 7SHIELD will be an integrated yet flexible and adaptable framework enabling the deployment of innovative services for cyber-physical protection of ground segments, such as e-fences, passive radars and laser technologies, multimedia AI technologies, that enhance their protection capabilities, while integrating or interoperating with existing protection solutions already deployed at their installations. The framework will integrate advanced technologies for data integration, processing, and analytics, machine learning and recommendation systems, data visualization and dashboards, data security and cyber threat protection. The technological solution is co-designed with first responders’ teams and contributes to policy making, standardisation and new guidelines for contingency planning and service continuity. The project will be evaluated and demonstrated in five installations of ground segments of space systems.

New catalogues of nearly daily temporal data will soon dominate the global archives. However, there has been little exploration of Deep Learning (DL) techniques to leverage the spatiotemporal dimension at scale. Training data remains rare relative to the spatiotemporal sampling which is necessary to adequately capture natural and man-made phenomenology latent in these large volumes of high cadence data. The project will establish the foundations for the next generation of rapid cadence land monitoring applications by: 1. Creating the most complete and dense spatiotemporal training set, combining Sentinel-2 with high cadence, very high resolution, harmonized multispectral Planet imagery at 500,000 patch locations over Europe, and open sourcing these datasets for the benefit of the entire remote sensing community. 2. Developing and benchmarking alternative ways of detecting and classifying change from very high cadence observations by training state-of-the-earth multiscale supervised and unsupervised DL classifiers on these unique data sources. 3. Delivering high cadence high resolution change detection heatmaps for the entire European continent. 4. Demonstrating a highly effective end-to-end process to monitor and update the CORINE land cover product, with emphasis on improved understanding of land use, speeding up update cycles and reducing maintenance costs. Our framework constitutes a game changer in the ability to derive time-critical and location-specific insights into dynamic land surface processes. Our ambition is to enable new and better ways of measuring and understanding the human footprint on our planet, which is a key challenge of the UN Sustainable Development Goals. This project brings together industry leaders with a strong, demonstrated record of disruptive innovations and young innovators: Planet Labs, Vision Impulse, VITO, IIASA and ONDA DIAS/Serco Italia.

The main objective of MOSES is to put in place and demonstrate at the real scale of application an information platform devoted to water procurement and management agencies (e.g. reclamation consortia, irrigation districts, etc.) to facilitate planning of irrigation water resources, with the aim of: • saving water; • improving services to farmers; • reducing monetary and energy costs. To achieve these goals, the MOSES project combines in an innovative and integrated platform a wide range of data and technological resources: EO data, probabilistic seasonal forecasting and numerical weather prediction, crop water requirement and irrigation modelling and online GIS Decision Support System. Spatial scales of services range from river basin to sub-district; users access the system depending on their expertise and needs. Main system components are: 1. early-season irrigated crop mapping 2. seasonal weather forecasting and downscaling 3. in-season monitoring of evapotranspiration and water availability 4. seasonal and medium/short term irrigation forecasting Four Demonstration Areas will be set up in Italy, Spain, Romania and Morocco, plus an Indian organization acting as observer. Different water procurement and distribution scenarios will be considered, collecting data and user needs, interfacing with existing local services and contributing to service definition. Demonstrative and training sessions are foreseen for service exploitation in the Demonstration Areas. The proposed system is targeting EIP on Water “thematic priorities” related to increasing agriculture water use efficiency, water resource monitoring and flood and drought risk management; it will be compliant to INSPIRE. This SME-led project address to the irrigated agriculture users an integrated and innovative water management solution.

Artificial Intelligence (AI) is already part of our lives and is extensively entering the space sector to offer value-added Earth Observation (EO) products and services. Copernicus data and other georeferenced data sources are often highly heterogeneous, distributed and semantically fragmented. Large volumes of satellite data (images and associated metadata) are frequently coming to the Earth from Sentinel constellation, offering a basis for creating value-added products that go beyond the space sector. The analysis and data fusion of all streams of data need to take advantage of the existing DIAS and HPC infrastructures, as well as the Galileo-enabled mobile devices when required by the involved end users to deliver fully automated processes in decision support systems. CALLISTO project integrates Copernicus data, already indexed in DIAS platforms such as ONDA-DIAS, utilising High Performance Computing infrastructures for enhanced scalability when needed. Complementary distributed data sources involve Galileo positioning data, visual content from UAVs, Web and social media data linking them with open geospatial data, in-situ sensor data. On top of these data sources, AI methods are applied to extract meaningful knowledge such as concepts, changes, activities, events, 3D-models, videos and animations of the user community. AI methods are also executed at the edge, offering enhanced scalability and timely services. The analysis of the extracted knowledge is performed in a semantic way and the associated analytics are delivered to the end users in non-traditional interfaces, including Augmented Reality, Virtual Reality and Mixer Reality in general. Data fusion among several types of data sources is provided on-demand, based on the end user requirements. The AI methods are trained to offer new virtual and augmented reality applications to water utility operators, journalists for the media sector, EU agriculture and CAP policymakers, and security agencies.