Integrating Drone Imaging and Deep Learning with Augmented Reality for Disaster Damage Assessment and Response

In the aftermath of natural disasters, accurate assessment of structural damage and identification of critical areas are essential for effective rescue operations. This research introduces a framework that combines drone-based imaging, deep learning analytics, and augmented reality (AR) to enhance disaster response. The process begins with unmanned aerial vehicles (UAVs) capturing high-resolution images of disaster-stricken areas, including collapsed buildings and obstructed pathways. These images are processed using deep learning models specifically designed to assess damage severity and identify critical structures requiring immediate attention. The insights generated by the deep learning models are seamlessly integrated into an AR application, which overlays this information onto the physical environment. First responders equipped with mobile devices can access real-time, actionable intelligence, improving situational awareness and decision-making on the ground. Preliminary simulations and field tests demonstrate the framework’s potential to streamline rescue operations and enhance disaster response capabilities. The findings demonstrate the system’s potential to effectively streamline disaster management operations and enable rapid response.