A Hybrid Swin–Mamba UNet for Post-Disaster Building Damage Assessment

Natural disasters frequently cause significant building damage, necessitating timely and accurate damage assessment for effective rescue operations and post-disaster reconstruction. Traditional building damage assessment methods commonly rely on paired pre- and post-disaster remote sensing images, which often face practical challenges in data acquisition and image pairing during emergency situations. To overcome these limitations, a hybrid swin–mamba U-shaped network (UNet) is developed for building damage assessment using only post-disaster remote sensing imagery. The proposed framework employs a Swin Transformer as the encoder to extract multi-scale features and capture long-range contextual information, while a Parallelized Patch-Aware Attention (PPA) convolution module is introduced in the decoder to restore spatial details and improve feature reconstruction. In addition, a Visual State Space (VSS) module is incorporated in the bottleneck layer to effectively model both global contextual dependencies and local structural information, thereby improving the representation of building damage characteristics from single-temporal imagery. Experiments conducted on the xBD dataset show that the proposed method outperforms the Swin–Unet by 1.7% in overall F1-score, achieving an overall F1-score of 55.2%. In addition, qualitative visualization results suggest that the proposed method has favorable generalization capability across different disaster scenarios. These results highlight the practical potential of the proposed framework for rapid post-disaster building damage assessment, particularly in emergency response scenarios where only post-disaster imagery is available.