DRIFT: Disaster Reconnaissance & Intelligent Forecasting Technology

After a disaster strikes, engineers will soon be able to assess building damage in real time by feeding drone-captured 2D images into an AI system that instantly maps structural vulnerability. Current post-disaster assessments rely on slow, manual inspections that delay emergency response and waste management. The DRIFT consortium—bringing together partners from South Korea, Türkiye, and the UK—aims to replace that bottleneck with a digital framework that processes visual data during the emergency phase itself. The project also tackles two linked problems: seismic vulnerability mapping, by harmonising risk data from multiple sources, and post-disaster waste management, by converting demolition rubble into usable reconstruction materials. If successful, the system will produce a QGIS-based tool that emergency planners and local authorities can use to prioritise where to send rescue teams, which buildings to cordon off, and how to recycle debris efficiently. The technology targets the quiet infrastructure of disaster response—the maps, databases, and logistics that determine how quickly a community can stabilise after an earthquake or explosion. The project is applied engineering, not fundamental science, and its value lies in shortening the gap between a disaster and an informed, coordinated recovery.