Enhancing preparedness for large-scale cross-border disasters

Expected outcome

Project results are expected to contribute to some or all of the following expected outcomes: Utilization of virtual and augmented reality training to simulate large-scale and transboundary disasters, improving disaster management’s readiness and response capabilities; Development of diverse crisis scenarios using VR/AR technology, providing immersive, practical training experiences for emergency personnel, responders and decision makers; Strengthened ability of responders and decision-makers to manage large-scale, cross-border disaster scenarios through advanced, technology-driven training programs; Improved knowledge/experience-sharing from past emergencies to cope with future emergencies, also strengthening trans-national knowledge and data exchange among EU countries as well as from early warning to early action.

Scope

The scope of this topic is on enhancing preparedness for large-scale, cross-border disasters by leveraging advanced training methodologies, including virtual reality (VR) and augmented reality (AR) simulations and Digital Twins. The objective is to improve the readiness and response capabilities of disaster responders and emergency management personnel by providing immersive, technology-driven training experiences that replicate complex disaster scenarios. Proposals should develop and validate innovative VR/AR-based crisis simulation models that can accurately depict diverse large-scale disaster situations, including transboundary hazards such as wildfires, floods, earthquakes, and industrial accidents. These simulations should incorporate near real-time data, AI-driven scenario adaptation, and multi-user interaction capabilities to ensure realistic, high-impact training exercises. Special attention should be given to the interoperability of these training platforms, allowing emergency services from different regions and countries to collaborate in joint preparedness exercises. Recommendations taken into account a gender-sensitive approaches should also improve crisis simulations by considering diverse needs and experiences. Research should also explore how digital training environments can enhance situational awareness, decision-making, and coordination among disaster management and relevant authorities. The integration of gamification techniques, AI-driven coaching systems, and real-time performance assessment should be considered to maximize learning outcomes and adaptation to evolving crisis scenarios linking to previous themes (e.g. biometrics and long-lasting disturbance, or whole of the society security, multi-hazard theme etc.). Furthermore, proposals should focus on strengthening knowledge and experience-sharing mechanisms across EU member states by developing transnational training frameworks and crisis management protocols, in coordination with the UCPM Training Programme . This should include the establishment of digital platforms and collaborative networks to facilitate the exchange of lessons learned from past emergencies, fostering continuous improvement in disaster response strategies. Projects should conduct a stakeholder or market analysis and a roadmap or plan for uptake of the developed methodologies, findings, and technologies to the industry, the research and innovation community, and/or the relevant authorities. Ethical, legal, and social aspects related to the use of immersive training technologies should also be addressed, ensuring compliance with data protection regulations and maximizing public trust in the adoption of these tools. Trust should also refer to the security of the tools, that should include appropriate measures for resilience against emerging cryptographic threats.

What this means for cities and regions

Disaster responders, emergency-management bodies and the regional authorities that coordinate across borders. Topic budget €8 million; 2 grants expected; around €4 million per project. Opens 5 May 2027; closes 4 November 2027. Realistic simulations and digital twins are built on terrain, land-cover and past-event data from Earth observation.