• Proposals should focus on the development of innovative AI algorithms that move beyond rigid functions, employing instead a dynamic set of descriptive building features derived from digital models (e.g., geometrical parameters, urban morphology, socio-economic indicators). These algorithms should be integrated with advanced, high-resolution hazard models — including hydrological and hydraulic models — tailored to the specific characteristics of the local area.
• The Local Digital Twins will enable:
  • Flood damage models capable of calculating building-scale impacts, forming the basis for damage hotspot maps.
  • Interactive user interfaces that allow components to be exchanged, modified, and reconfigured to estimate flood damage under various urban planning and risk management scenarios — for example, assessing the feasibility of proposed or existing constructions in flood-prone zones and recommending targeted mitigation strategies.

The scope of this topic includes a strong research and innovation component aimed at the prototyping, testing, and large-scale validation of tailored AI algorithms designed to model multiple disaster types, with a focus on operational deployment in real-world contexts. .It is recommended to prioritise the use of frugal (and local) AI as much as possible. This approach will both reduce greenhouse emissions -an indirect driver of climate-related disasters- and ensure that the tools remain functional in environments with limited connectivity.

Proposals should take into account the expertise of the European Commission’s Joint Research Centre (JRC), particularly its experience in developing global systems for disaster and risk management and analyse the potential uptake of the project outcomes by the Copernicus Emergency Management Service. In addition, proposals should align with for the 2025 Mission call on Local Digital Twin for urban planning, ensuring interoperability and complementarity with related European initiatives.

The project results should be modular for reuse in locations outside Europe considering constraints on deployment of AI solutions in low- and middle-income countries. Therefore, the project results shall be open source as much as possible and transferable through open platforms.

1. Focus will be on open-source solutions (both software and hardware) and their integration into existing platforms (e.g. EDIC) to ensure replicability of the results and portability in different areas.
2. The proposal should support open-source software and open hardware design. Applicants are encouraged to support, open access to data, access to testing and operational infrastructures as well as an IPR regime ensuring lasting impact and reusability of results.

Beneficiaries that intend to transfer ownership or grant an exclusive licence must formally notify the granting authority (i.e. DG-CNECT and HaDEA) before the intended transfer or licensing takes place and the granting authority may up to four years after the end of the action object to a transfer of ownership or the exclusive licensing of results.