Building Damage Assessment

Building damage assessment utilizes high-resolution satellite imagery and advanced algorithms to rapidly and accurately determine the extent of structural damage following natural disasters or conflicts. Current research heavily employs deep learning models, particularly convolutional neural networks (CNNs) and transformer architectures, often within a Siamese or U-Net framework, to analyze pre- and post-event imagery and even incorporate causal relationships between damage and environmental factors. These automated methods significantly improve upon manual assessment, offering faster response times and more comprehensive damage mapping crucial for efficient resource allocation and humanitarian aid. The development of robust, generalizable models that handle diverse damage types and image qualities remains a key focus.