Automatic Detection of Natural Disasters Using Faster R-CNN with ResNet50 Backbone

Abstract

Natural disasters pose significant threats to human life and infrastructure. Timely detection and assessment of these events are crucial for effective disaster management. This study proposes an automatic detection system for natural disasters using aerial imagery. Accurate and timely detection of natural disasters is critical for minimizing their impact and supporting emergency response efforts. This study presents a comparative analysis of deep learning architectures for natural disaster detection using satellite and aerial imagery. Four models were evaluated as baseline CNN, ResNet50, Faster-CNN, and Faster R-CNN with a ResNet50 backbone using standard classification metrics. The results demonstrate that deeper and more sophisticated models significantly enhance detection performance. While the baseline CNN achieved modest results with 85.3% accuracy, integrating residual learning in ResNet50 improved accuracy to 92.7%. Region-based models further boosted performance, with Faster-CNN and Faster R-CNN attaining 95.1% and 97.1% accuracy, respectively. The superior performance of the Faster R-CNN with ResNet50 highlights its robustness and suitability for real-time disaster monitoring, offering a scalable and reliable solution for operational deployment in disaster management systems.