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

View Full Paper

Ask AI

Bookmark

Overall precision of 0.892 was achieved in detecting structural damage from images captured by UAVs, indicating high accuracy.
Using deep learning techniques like YOLOv9, the model specialized in identifying collapsed structures among various building states.
Cross-validation methods enhanced the model's performance, ensuring reliable results for post-earthquake assessments.
This approach enhances disaster recovery and urban planning, supporting timely emergency responses through automated assessments.

Discussion

No takes yet. Share an insight, caveat, or question.

This analysis demonstrates deep learning improves damage detection in structures after earthquakes, highlighting UAV imagery's utility.

Bayraktar et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark