Building Damage

Building damage assessment aims to rapidly and accurately determine the extent of structural damage following natural disasters or other events, primarily to inform emergency response and resource allocation. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), including U-Net and Siamese network architectures, often enhanced with techniques like self-supervised learning and graph convolutional networks to improve efficiency and generalization across different disaster types and image sources. This field is crucial for improving disaster response and recovery efforts, with ongoing work focusing on minimizing the need for extensive labeled data and enhancing the reliability and interpretability of automated damage detection methods.