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30 September 2024 Detecting collapsed buildings caused by earthquake from remote sensing image based on deep learning
Lifu Zheng, Guichun Luo, Qingquan Tan, Bozhi Zhang, Xiaoran Lv
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Proceedings Volume 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024); 1328610 (2024) https://doi.org/10.1117/12.3045198
Event: Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 2024, Guangzhou, China
Abstract
After an earthquake, rapid detection of collapsed building distribution from remote sensing images is crucial for government agencies, as it facilitates effective rescue operations and minimizes casualties. Traditional methods of damage assessment methods, such as on-site surveys and manual interpretation of remote sensing data, are often hindered by their labor-intensive and time-consuming nature, thus necessitating a more efficient approach. In this study, we address this challenge by employing a deep learning-based methodology for detecting collapsed buildings from satellite imagery. Specifically, we developed a Deep Learning model based on the U-net architecture, chosen for its high flexibility and performance. Utilizing a custom weighted loss function, our model achieved an Average Precision of 0.871 and an Average Recall of 0.893 on the test set. Application of the model to the Turkey earthquake case demonstrated rapid and accurate segmentation of most buildings. This study suggests that integrating U-net-based deep learning with satellite images can provide the precise distribution of collapsed buildings needed for earthquake emergency management.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Lifu Zheng, Guichun Luo, Qingquan Tan, Bozhi Zhang, and Xiaoran Lv "Detecting collapsed buildings caused by earthquake from remote sensing image based on deep learning", Proc. SPIE 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 1328610 (30 September 2024); https://doi.org/10.1117/12.3045198
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KEYWORDS
Buildings
Earthquakes
Deep learning
Image segmentation
Remote sensing
Satellite imaging
Satellites
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