Bitemporal Attention Transformer for Building Change Detection and Building Damage Assessment

Building change detection (BCD) holds significant value in the context of monitoring land use, whereas building damage assessment (BDA) plays a crucial role in expediting humanitarian rescue efforts post-disasters. To address these needs, we propose the bitemporal attention module (BAM) as an innovative cross-attention mechanism aimed at effectively capturing spatio-temporal semantic relations between a pair of bitemporal remote sensing images. Within BAM, a shifted windowing scheme has been implemented to confine the scope of the cross-attention mechanism to a specific range, not only excluding remote and irrelevant information but also contributing to computational efficiency. Moreover, existing methods for BDA often overlook the inherent order of ordinal labels, treating the BDA task simplistically as a multiclass semantic segmentation problem. Recognizing the vital significance of ordinal relationships, we approach the BDA task as an ordinal regression problem. To address this, we introduce a rank-consistent ordinal regression loss function to train our proposed change detection network, bitemporal attention transformer. Our method achieves state-of-the-art accuracy on two BCD datasets (LEVIR-CD+ and S2Looking), as well as the largest BDA dataset (xBD).