Post-Earthquake Structural Reconnaissance Using UAV Imagery and Deep Learning: A Case Study of the Kahramanmaraş Earthquake

Rapid damage assessment is essential for emergency action and recovery because earthquakes seriously harm urban infrastructure. A dataset for identifying collapsed structures was developed using Unmanned Aerial Vehicle (UAV) photos from atlas.gov.tr after the February 6th Kahramanmaras earthquake. With an emphasis on collapsed structures, YOLOv9, a sophisticated object detection model, was trained to categorize various building states. Across data subsets, cross-validation approaches were used to improve model performance and generalization. The model's efficacy for post-earthquake damage detection was demonstrated by its maximal overall precision of 0.892 and detection precision of 0.82 for collapsed structures. Rapid evaluation is made possible by combining deep learning with UAV imagery, which aids in disaster recovery, urban planning, and emergency response. By guaranteeing precise structural damage identification and supporting well-informed decision-making, this method improves catastrophe management. The findings demonstrate deep learning's potential for automating extensive damage assessment.