U2DDS‐Net: A New Architecture Based on U2Net With Disaster Type for Building Damage Assessment Under Natural Disasters

Building damage assessment in the face of natural disasters is crucial for economic development, disaster relief, and post‐disaster reconstruction. However, existing algorithms often overlook the impact of the disaster class when extracting difference features from high‐resolution pre‐ and post‐disaster image pairs obtained through satellite remote sensing, without considering the influence of the disaster type, that is, the different ways in which different disasters affect buildings. To address this limitation, we propose U2DDS‐Net, a two‐stage model based on U2Net and Swin Transformer. In stage 1, U2Net locates and segments buildings in pre‐disaster images. In stage 2, we enhance the model with the disaster‐type token and the diff swin stage module, which consider the disaster type and extract difference information at multiple scales. Experimental results on the xBD dataset demonstrate the significant improvement achieved by our approach, surpassing state‐of‐the‐art performance. Our research fills the gap by considering specific disaster types, and our two‐stage model provides accurate building damage assessment across various disaster scenarios.