Building Damage Detection with UNet-Backbone Fusion in High-Resolution Satellite Imagery: 2023 Morocco Earthquake - S. Holail; T. Saleh; T. Saleh; X. Xiao; A. H. Ali; D. Li (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2025)
Earthquakes and other natural disasters rank among the most destructive events, causing widespread loss of life and severe economic consequences globally. A primary consequence of earthquakes is the large-scale collapse and damage of buildings. The rapid advancement of high-resolution remote sensing technology, offering extensive coverage and multi-temporal capabilities, combined with deep learning methods, has opened new possibilities for accurately and efficiently detecting and assessing building damage to support crisis management. However, pre- and post-disaster images are often acquired under varying temporal, lighting, and weather conditions, complicating the task of accurately identifying building damage levels. This study proposes a Siamese network based on UNet to address these challenges, enabling the assessment of building damage using satellite imagery following earthquakes. The network leverages multi-scale feature differentiation to model spatial and temporal semantic relationships, addressing the issue of intra-class semantic variation. The proposed method was evaluated on the xBD disaster damage dataset and the 2023 Morocco earthquake dataset, achieving an overall accuracy of 95.5% and a kappa coefficient of 76.0%. These results highlight the potential of AI-driven solutions to meet the critical demands for speed and accuracy in disaster response scenarios.