Deep Learning-Based Collapsed Building Mapping from Post-Earthquake Aerial Imagery

Rapid building damage assessments are vital for an effective earthquake response. In Japan, traditional Earthquake Damage Certification (EDC) surveys—followed by the issuance of Disaster Victim Certificates (DVCs)—are often inefficient. With advancements in remote sensing technologies and deep learning algorithms, their combined application has been explored for large-scale automated damage assessment. However, the scarcity of remote sensing data on damaged buildings poses significant challenges to this task. In this study, we propose an Uncertainty-Guided Fusion Module (UGFM) integrated into a standard decoder architecture, with a Pyramid Vision Transformer v2 (PVTv2) employed as the encoder. This module leverages uncertainty outputs at each stage to guide the feature fusion process, enhancing the model’s sensitivity to collapsed buildings and increasing its effectiveness under diverse conditions. A training and in-domain testing dataset was constructed using post-earthquake aerial imagery of the severely affected areas in Noto Prefecture. The model approximately achieved a recall of 79% with a precision of 68% for collapsed building extraction on this dataset. We further evaluated the model on an out-of-domain dataset comprising aerial images of Mashiki Town in Kumamoto Prefecture, where it achieved an approximate recall of 66% and a precision of 77%. In a quantitative analysis combining field survey data from Mashiki, the model attained an accuracy exceeding 87% in identifying major damaged buildings, demonstrating that the proposed method offers a reliable solution for initial assessment of major damage and its potential to accelerate DVC issuance in real-world disaster response scenarios.