Abstract: Natural disasters occur frequently, posing serious threats and causing substantial losses to human society. As the primary carriers of population and economic activities, buildings are highly vulnerable to disasters, and their damage status directly affects emergency response and post-disaster reconstruction. Therefore, rapid and accurate acquisition of building damage information has become a critical requirement in disaster management. To address the challenges of geometric misalignment, background interference, and feature alignment difficulties in multi-temporal and cross-modal remote sensing imagery, we propose a Pre-Disaster Footprint–Guided Change-Aware Damage Detection Network (PDF-Net). Specifically, the framework first employs a twin pyramid vision transformer to extract multi-level features from pre- and post-disaster imagery and generates pre-disaster building masks to introduce guidance information. Subsequently, a change-aware gated attention module is designed to enhance differential representation of low-level detail features, highlighting local changes, while a grouped cross-temporal attention mechanism with overlapped windows is introduced to explicitly align high-level semantic features, thereby reinforcing the structural change representation of buildings. Finally, fine-grained damage detection is achieved through cross-level feature fusion. Experiments conducted on the xBD dataset (pre-disaster optical – post-disaster optical) and the BRIGHT dataset (pre-disaster optical – post-disaster SAR) demonstrate that the proposed method achieves significant improvements in both intra-modal and cross-modal tasks. Specifically, B-PriorNet surpasses the current state-of-the-art methods by 0.58% in mean Intersection-over-Union (mIoU) on the xBD dataset and by 1.97% on the BRIGHT dataset, showing stronger robustness and generalization ability in cross-modal detection scenarios. These results validate the effectiveness and practical value of the proposed framework in complex disaster environments.

Key words: Remote sensing, deep learning, disaster, change detection, building damage