Classification of Disaster Aerial Image for Efficient Disaster Management

Abstract

Natural and man-made disasters are unexpected events that concern nations worldwide. Every year, disasters result in infrastructural damages, monetary costs, population displacement, injuries, and deaths. Unfortunately, climate change strengthens the destructive power of disasters. The first 72 hours are crucial for rescuing survivors after the occurrence of the disaster. Lack of appropriate and rapid assistance to disaster victims due to ineffective identification and assessment of disaster struck areas makes it very difficult to plan and execute rescue missions. Unmanned Aerial Vehicles (UAVs) with embedded Graphical Processing Units (GPUs) and high resolution cameras provide ease in remotely sensing the disaster area and autonomy in decision making in disaster recovery and management. However, on-board embedded GPUs have some limitations in terms of computational power, storage, power consumption, and size. In this research, we improved cutting-edge image classification models for effective on-board integrated GPU implementation. The outcomes of the experiments show that the optimized compressed model is up to 92 times faster than the native model, which has a throughput improvement of almost 99 percent. Additionally, the proposed architecture allows for a model size reduction of roughly 84 percent without sacrificing accuracy.