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Expected Outcomes

Projects funded under this topic are expected to deliver AI-based solutions that proactively predict, respond to, and mitigate the impacts of natural disasters, enhancing citizen protection and urban resilience. Specific outcomes include:

• Proactive disaster management: Development of open AI models that support decision-making before disasters occur, enabling authorities to implement preparedness strategies and urban planning measures that minimize risks to citizens.
• Enhanced flood protection: Improved urban and riverine flood prediction using AI-driven Local Digital Twins, allowing precise simulations, scenario planning, and risk mitigation strategies for urban areas.
• Integrated environmental and urban data: Advanced integration of datasets including terrain, land cover, urban features, soil characteristics, real-time meteorology, and river hydraulics to enhance flood analysis and climate change adaptation strategies.
• High-resolution forecasting: Leveraging cutting-edge meteorological and climate models, and drawing on initiatives such as the Global Flood Awareness System and Destination Earth, to assess extreme weather events and flooding scenarios effectively.

Scope

In alignment with the EU Apply AI Strategy, proposals should develop and implement innovative AI algorithms from concept through large-scale testing and validation. Projects will focus on Local Digital Twins for flood preparedness, enabling simulation of urban flooding, identification of at-risk areas, and estimation of potential damage to inform planning and mitigation.

Key features of proposed solutions include:

• Dynamic AI algorithms: Moving beyond rigid functional models, algorithms should incorporate descriptive building features derived from digital urban models, including geometry, morphology, and socio-economic indicators.
• Advanced hazard integration: Integration with high-resolution hydrological and hydraulic models tailored to local urban characteristics.
• Flood damage modeling: Building-scale damage assessment and hotspot mapping to guide risk management and urban planning decisions.
• Interactive scenario tools: User interfaces that allow stakeholders to simulate various planning and mitigation scenarios, including assessing proposed constructions in flood-prone zones.
• Frugal and modular AI deployment: Prioritize lightweight, low-emission AI solutions that are functional in low-connectivity environments and easily transferable to locations outside Europe, including low- and middle-income countries.
• Open-source outputs: Ensure results are accessible through open platforms to maximize reuse and knowledge transfer.

Projects should also leverage the expertise of the European Commission’s Joint Research Centre (JRC), particularly its experience in disaster and risk management systems, and align with the Copernicus Emergency Management Service and the 2025 Mission call for Local Digital Twins for urban planning.

The focus is on research, prototyping, testing, and operational validation of AI algorithms in real-world urban environments, enabling scalable, practical solutions for disaster resilience, climate adaptation, and citizen safety.