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.

There is growing awareness of the importance of having all sectors of society adapt to face a range of global environmental and climate challenges, taking into account intergenerational justice. These challenges require developing an encompassing framework for research and innovation (R&I) to address environmental and climate ethics and integrity issues, which relate not only to R&I involving potentially significant environmental and climate repercussions (e.g., R&I in the area of electro-magnetic fields), but also R&I specifically aiming to develop knowledge and technologies to address environmental and climate challenges (e.g., geoengineering or biotechnology in food systems). RE4GREEN’s main goal is to contribute to a European Research Area (ERA) ethics and integrity framework for research and innovation activities designed to support the transition to a sustainable economy and society as envisioned by the European Green Deal. While R&I has too often been part of the problem of environmental degradation and biodiversity loss, a central pillar of the European Green Deal and associated legislation is to promote new technologies and sustainable solutions to reach net zero emissions in the EU by 2050, and advance a range of other climate and environmental objectives. Based on a bottom-up approach that uses the social lab methodology to reflect diverse stakeholder expertise, RE4GREEN will develop operational research ethics and integrity guidelines, recommendations and training materials and programmes for researchers, ethics and integrity experts and advisors and ethics review boards to make sure R&I activities more holistically contribute solutions toward the Green Transition.

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.

unCoVer is a functional network of research institutions collecting data derived from the provision of care to COVID-19 patients by health systems across Europe and internationally. These real-world data allow for studies into patient’s characteristics, risk factors, safety and effectiveness of treatments and potential strategies against COVID-19 in real settings, and complement findings from efficacy/safety clinical trials where vulnerable groups, and patients with comorbidities are often excluded. The network will facilitate access to otherwise scattered datasets, and build computational and analytical platforms to streamline studies on risk characterisation, and prediction modelling using standardised pooled data derived from real life practices. It will fill data gaps, unify current initiatives and create downstream exploitation opportunities for researchers and public health strategies to optimise COVID-19 strategies and minimise the impacts of future outbreaks