E2mC aims at demonstrating the technical and operational feasibility of the integration of social media analysis and crowdsourced information within both the Mapping and Early Warning Components of Copernicus Emergency Management Service (EMS). The Project will develop a prototype of a new EMS Service Component (Copernicus Witness), designed to exploit social media analysis and crowdsourcing capabilities to generate a new Product of the EMS Portfolio. The purpose of the new Copernicus Witness Service Component is to improve the timeliness and accuracy of geo-spatial information provided to Civil Protection authorities, on a 24/7 basis, during the overall crisis management cycle and, particularly, in the first hours immediately after the event. This will result in an early confirmation of alerts from running Early Warning Systems as well as first rapid impact assessment from the field. The technological enabler of the Copernicus Witness is the innovative and scalable Social&Crowd (S&C) Platform, developed by E2mC. Heterogeneous social media data streams (Twitter, Facebook, Instagram,… and different data: text, image, video, …) will be analysed and sparse crowdsourcing communities will be federated (crisis specific as Tomnod, HOT, SBTF and generic as Crowdcrafting, EpiCollect,…). Two demonstration loops will validate the usefulness of Copernicus Witness and the S&C Platform suitability to allow EC to evaluate possible Copernicus EMS evolution options. E2mC will perform demonstrations within realistic and operational scenarios designed by the Users involved within the Project (Civil Protection Authorities and Humanitarian Aid operators, including their volunteer teams) and by the current Copernicus EMS Operational Service Providers that are part of the E2mC Consortium. The involvement of social media and crowdsourcing communities will foster the engagement of a large number of people in supporting crisis management; many more citizens will become aware of Copernicus.

This project will build the WeatherGenerator – the world’s best generative Foundation Model of the Earth system – that will serve as a new Digital Twin for Destination Earth. The WeatherGenerator will be based on representation learning and create a general and versatile tool that models the dynamics of the Earth system based on a large variety of Earth system data. The WeatherGenerator will be task-independent and will improve results for a wide range of machine learning applications when compared to task specific machine learning tools. It will also be more resilient for climate applications when the underlying data distributions are changing, and it will lead to a significant reduction in computational costs and faster turnaround times. To achieve this, we will: (1) Collect and use the most important datasets of Earth system science including data from Digital Twins of Destination Earth, selected observations, analysis and reanalysis datasets, and output of conventional Earth system models. (2) Build the WeatherGenerator as a novel representation learning-based machine learning tool that exploits the full potential of Europe’s largest supercomputers. (3) Engage with the wider community via services and apply the WeatherGenerator for 22 selected applications that can be integrated into the Destination Earth framework. The applications include global and local predictions, local downscaling, data assimilation, model post-processing, and impact applications in the domains of renewable energy, water, health and food. The project consortium that will build the WeatherGenerator consists of experts in machine learning, supercomputing and Earth system sciences, and includes industry, SMEs, and leading operational weather centers. The WeatherGenerator will lead to key innovations in weather and climate science and machine learning to enable Europe to establish and defend leadership with respect to machine-learning based Earth system modelling.

Increasing vulnerability due to climate change requires cross-sectoral management of the territories with bottom-up approaches based on active community participation. Climate change intensifies territorial risk and impacts severely on human migration and displacement, particularly in sub-Saharan Africa. Avoiding maladaptation associated to internal migration as well as to other adaptation actions which do not adopt a nexus approach (e.g. water-food-energy nexus) requires effective planning and management of migration hot spots. At the same time, it calls for more integrated strategies which simultaneously support sustainable development growth pathways and climate resilience development in the African context. ALBATROSS aims at addressing these challenges by delivering novel, innovative and decision making-relevant tools and knowledge which will support the adoption of feasible, effective, and sustainable need-based adaptation strategies into AF policies and programs. It will do that by co-creating climate services and tools which will enable the weighting of compound and cascading impacts (benefits and threats) on ecosystem health, food security, socio-economic vulnerability while addressing specific natural hazards (e.g., drought or flood) or sectoral climate adaptation needs (e.g., agriculture) through nature-based solutions (NBS) and other climate adaptation measures. The methodological approach of ALBATROSS will be tested ad validated in different Sub-Saharan countries, leading to the co-creation and co-evaluation with key stakeholders of need-based adaptions options and plans. By combining participatory approaches with fundamental research grounded on the latest generation of observational data and modelling techniques, ALBATROSS will provide robust and science-based evidence on the degree at which NBS may perform as multifunctional adaptation strategy for counteracting forced migration, biodiversity loss and hydrometeorological risk in Sub-Saharan Africa.

Climate change phenomena represent the most serious threats to human well-being and sustainable development. This affects several dimensions of human life from individual health issues to economic growth, passing through civil protection, with strong impacts on European territories and in several other world regions. The I-CHANGE (Individual Change of HAbits Needed for Green European transition) project faces the challenge of engaging and promoting the active participation of citizens for addressing climate change, sustainable development and environmental protection in the framework of the European Green Deal , the European Climate Pact and the European Biodiversity Strategy for 2030. The overall driving concept is that citizens and civil society have a central role in the definition of environmental protection and climate action and their direct involvement is essential to drive a true shift and promotion of changes of behaviors towards more sustainable patterns. I-CHANGE represents a change of paradigm achievable through a multi-disciplinary and participatory approach: improvement of data usability - citizen awareness raises through the observation of the environmental impacts of human activities; active participation of citizens - the active involvement of citizens through a set of Living Labs (LLs) located in different socio-economic contexts; climate change awareness - the development of clear understanding for citizens of the scientific processes underlying climate change and environmental protections.

Natural and human-caused disasters, including uncontrolled urbanization pose multiple short-term and long-term risks, such as building inhabitants being vulnerable to heat or cold waves, older people or other vulnerable individuals struggling to reach safe places during emergencies, and citizens not receiving timely alerts from public authorities regarding impending flash floods, or firestorms, among others. The RETIME project aims to address impactful changes at both the contextual district level and individual homes. It will introduce a data-driven tool that aggregates existing data from weather stations, sensor networks, and satellite images, automated on-site surveys to simulate the impacts of current phenomena and future projections. The innovative aspect lies in an advanced computational analysis that generates prospective scenarios based on socio-architectural and environmental studies, combined with local, territorial remote and on-site surveys. RETIME will develop a suite of 4 innovative adaptation solutions for reducing risk in urban areas: 1) A sensor-based IT automated alert system; 2) A Digital Building Twin (DBT); 3) A digital Building Renovation Passport (BRP); 4) A Resilience Knowledge Hub and Decision Support platform. These tools will be sensitive and capable of prioritizing alerts based on the architectural and societal context-specific features in three pilot areas (in Portugal, Slovakia, and Estonia). RETIME will meet citizens' real-time needs while supporting control and decision-making processes by: identifying vulnerable groups and critical hotspots, enhancing building resilience; utilizing existing datasets and forecasting computational tools; delivering adaptation plans with a focus on carbon-neutral lifecycle awareness; and increasing citizens' understanding of natural disasters and their multi-scale impacts. Overall, RETIME strives to create a more resilient, informed, and prepared urban environment for all citizens.