Research progress on post-earthquake building collapse detection and buried person assessment based on multisource remote sensing data

Earthquakes, with their sudden onset and destructive power, pose severe threats to human lives and property. Rapid and accurate assessment of building damage and prediction of trapped populations are critical for efficient emergency response and post-disaster reconstruction. This paper reviews recent advancements in post-earthquake building damage detection and human entrapment assessment using multisource remote sensing data. It focuses on core technical challenges and cutting-edge solutions, including cross-modal registration of optical and Synthetic Aperture Radar (SAR) imagery, fine-grained building damage identification, and dynamic assessment models for trapped populations. Research demonstrates that the deployment of edge computing and the star-space-ground collaborative observation system can significantly improve emergency response timeliness. Finally, the paper outlines future research directions in this field, such as small-sample transfer learning and multi-hazard coupled assessment models.