Imagine having a live, 3D digital map of a forest fire or flood that updates instantly using drones and social media. It's like a high-tech weather map, but it also predicts where the disaster will move next so rescuers know exactly where to go. It turns a mess of confusing data into a clear picture that helps leaders make fast, life-saving decisions.
Emergency managers struggle with slow, fragmented data during disasters, leading to delayed responses. Existing tools often lack the speed and precision needed to predict disaster evolution in real-time.
A software platform producing precise 3D digital twins of disaster sites integrated with Extended Reality (XR) visualization and predictive analytics for wildfires and floods.
If you are a city agency dealing with unpredictable urban flooding — this project developed a 3D disaster area map that fuses 10 distinct data sources to provide real-time situational awareness. This allows for automated response strategies and faster evacuation planning.
If you are a land manager dealing with rapid wildfire spread — this project developed simulations for wildfire fronts and smoke dispersion that reduce computational costs. This helps in deploying resources more efficiently to protect assets.
If you are a drone operator dealing with complex data acquisition in disaster zones — this project developed automated response strategies for drone and satellite data acquisition. This optimizes flight paths and data collection for high-speed analytics.
Based on available project data, the project is moving toward a 'NDM-Analytics-as-a-Service' (NDM-AaaS) business model, though specific pricing is not listed.
Yes, the platform is designed to be scalable and has been specifically validated on two critical use-cases: wildfires and floods.
Based on available project data, the project involves 23 partners including 8 industry members, but specific licensing terms are not provided.
The system integrates data from smart drones, in-situ sensors, remote sensing, and meteorological tools, distributing computations across an edge-to-cloud continuum to minimize latency.
The project period runs from 2022-12-01 to 2026-11-30, with recent reports indicating a transition from lab research to operational field validation.
The consortium is well-balanced for commercialization, featuring 23 partners across 8 countries. With an industry ratio of 35% (8 companies, including 6 SMEs), there is a strong link between academic research and market application. The presence of 6 universities and 3 research organizations ensures deep technical expertise in AI and remote sensing.
Contact Aristotelio Panepistimio Thessalonikis in Greece
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