Empowering Disaster Management with AI-Driven Debris Detection and Prioritization

Natural disasters such as earthquakes, floods, and hurricanes result in catastrophic damage, leaving behind huge quantities of debris that hinder emergency response and recovery efforts. Identification and clearance of debris must be accomplished in a timely fashion to rebuild affected zones and reduce future hazards. Traditional approaches which make the process slow, tedious, and prone to errors. This project proposes an intelligent debris management system that uses AI-based image analysis and geolocation data to automate debris detection, classification, and prioritization of cleaning. The technology is equipped with real-time monitoring, which allows authorities and fieldworkers to better distribute resources and prioritize cleaning by severity and location. The system is live, enabling centralized coordination and real-time updates. Experimental results show that debris identification is more precise and faster compared to previous techniques. AI robustness will be improved in future research by taking into account more types of catastrophe scenarios and improving detection capability using aerial imagery.