AI-Driven Analysis of Drone Imagery for Flash Flood Damage Assessment

Natural disaster management is constrained by short response windows that hinder systematic observations, highlighting the need for rapid and effective tools to support emergency services. Drones are now an integral part of disaster management practices, providing valuable support for real-time emergency response, impact forecasting, and post-disaster damage assessment. Their integration with artificial intelligence (AI) further enhances these capabilities through fast, scalable, and accurate data processing. This study presents a real-world application of drone-based and AI-driven techniques for emergency management following the severe flash-flood event that affected Valencia (Spain) after a cut-off low event on 29 October 2024. A multi-sensor dataset combining RGB imagery, LiDAR, multispectral data, and gas measurements was used to: (i) generate a detailed topographic survey of the most affected area and support a basic flood simulation; (ii) identify and assess damaged infrastructure; (iii) quantify sludge accumulation zones and classify sludge types through machine-learning analysis of spectral responses; (iv) detect waste accumulation and vehicle clustering using deep-learning models; and (v) evaluate air quality in the most impacted urban sectors. The results demonstrate that integrating drone observations with AI-based analysis provides valuable near real-time information for disaster response and recovery. This approach improves situational awareness, supports more reliable decision-making, and enables more effective mitigation actions. The combination of these technologies offers a flexible, safe, and precise alternative to conventional remote sensing and field-based methods, helping to bridge critical gaps in post-disaster assessment and emergency management.