DPNet: A Disaster Perception Network for Building Damage Detection Using Single-Temporal Remote Sensing Imagery

Rapid detection of damaged buildings is critical for effective emergency response following sudden disasters. Although single-temporal methods enable rapid detection without requiring paired pre-event imagery, they often lack disaster-specific awareness, leading to limited accuracy and weak generalization. To address these critical limitations, we propose a disaster perception network (DPNet) tailored for single-temporal high-resolution remote sensing imagery, enabling more precise detection of damaged buildings in various disaster scenarios. DPNet integrates disaster perception module (DPM) to adaptively fuse disaster semantics with building damage features and employs the dynamic semantic-guided multi-task loss (DSML) to enforce cross-task semantic consistency, effectively reducing both false positives and missed detections. Extensive experiments conducted on a large-scale global dataset, encompassing 24 disaster events and 7 distinct disaster types, indicate that DPNet outperforms existing state-of-the-art models by over 7 % in mean intersection over union. Moreover, it significantly surpasses all comparative models on 7 additional unseen disaster events, further validating its generalization capability. These results demonstrate that DPNet enables robust building damage detection, providing a highly reliable and efficient technical solution for global disaster emergency response.