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<p>Earthquakes cause severe damage to urban infrastructure, making rapid damage assessment crucial for emergency response and recovery planning. Following the February 6th Kahramanmaras earthquake, UAV imagery from atlas.gov.tr was used to develop a dataset for detecting collapsed buildings. YOLOv9 and YOLOv12, advanced object detection models, were trained to classify different building states, focusing on collapsed structures. To enhance model performance and generalization, cross-validation techniques were applied across data subsets, and image mirroring was used to improve image diversity. The model demonstrated strong detection performance, achieving a peak overall precision of 0.965 and an F1-score of 0.937, with particularly reliable localization quality reflected by an mAP50-95 of 0.805. For collapsed buildings, the model reached a precision of 0.954 and an F1-score of 0.899, demonstrating its effectiveness for post-earthquake damage detection using YOLOv9-C. Integrating UAV imagery with deep learning enables rapid assessment, supporting emergency response, urban planning, and disaster recovery. This approach enhances disaster management by ensuring accurate structural damage detection and aiding informed decision-making. The results highlight the potential of deep learning in automating large-scale damage assessment.</p>

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Rapid Post-earthquake Damage Detection Using UAV Imagery and YOLO: A Case Study from the Kahramanmaras Earthquake

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Merve Bayraktar,
  • Cemil Zalluhoğlu,
  • Alper Aldemir,
  • Berna Unutmaz,
  • Burcu Güldür Erkal

摘要

Earthquakes cause severe damage to urban infrastructure, making rapid damage assessment crucial for emergency response and recovery planning. Following the February 6th Kahramanmaras earthquake, UAV imagery from atlas.gov.tr was used to develop a dataset for detecting collapsed buildings. YOLOv9 and YOLOv12, advanced object detection models, were trained to classify different building states, focusing on collapsed structures. To enhance model performance and generalization, cross-validation techniques were applied across data subsets, and image mirroring was used to improve image diversity. The model demonstrated strong detection performance, achieving a peak overall precision of 0.965 and an F1-score of 0.937, with particularly reliable localization quality reflected by an mAP50-95 of 0.805. For collapsed buildings, the model reached a precision of 0.954 and an F1-score of 0.899, demonstrating its effectiveness for post-earthquake damage detection using YOLOv9-C. Integrating UAV imagery with deep learning enables rapid assessment, supporting emergency response, urban planning, and disaster recovery. This approach enhances disaster management by ensuring accurate structural damage detection and aiding informed decision-making. The results highlight the potential of deep learning in automating large-scale damage assessment.