Building Damage Detection

Building damage detection aims to rapidly and accurately assess the extent of structural damage following natural disasters using remote sensing data like satellite imagery and SAR. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) for image analysis and segmentation, and recurrent neural networks (RNNs) for handling sequential data, with a growing interest in graph neural networks (GNNs) to leverage spatial relationships between buildings. These advancements enable automated, large-scale damage assessment, significantly improving the speed and accuracy of post-disaster response and resource allocation compared to traditional manual methods.