Post-Earthquake Building Collapse Detection and Damage Reporting Using Deep Learning

Abstract

Earthquakes cause large-scale structural damage and loss of life, making rapid and accurate damage assessment critical in disaster management processes. In this context, remote sensing (RS) and deep learning-based change detection (CD) methods have the potential to provide decision-makers with effective information during post-disaster response efforts. This study presents a comparative analysis of segmentation-based CD models for detecting building collapses following the earthquakes that struck Turkey on February 6, 2023. The high-resolution TUE-CD dataset, consisting of pre- and post-disaster satellite images, was utilized. Three different deep learning models were designed for change detection: an adaptation of the DeepLabv3 architecture for CD tasks, a U-Net-based model, and a custom architecture enhanced with attention mechanisms and multi-scale feature fusion. These models were evaluated to compare the impact of different architectural choices and components on performance. Additionally, a comprehensive damage report was generated to support post-disaster building damage analysis with quantitative data and facilitate interpretation. The report includes calculations based on segmentation masks derived from model predictions. © 2025 IEEE.