Abstract: Method for earthquake disaster area detection with just a single satellite-based SAR data which is acquired on the day after earthquake based on object detection method of YOLOv8 and Detectron2 is proposed. Through experiments with several SAR data derived from the different SAR satellites which observed Noto Peninsula earthquake occurred on the first of January 2024, it is found that the proposed method works well to detect several types of damages effectively. Also, it is found that the proposed method based on “Roboflow” and YOLOv8 as well as Detectron2 for annotation and object detection is appropriate for disaster area detection. Furthermore, it is possible to detect disaster areas even if just one single SAR data which acquired on the day after the disaster occurred because the trained learning model for disaster area detection is created through experiments.

Kohei Arai, Yushin Nakaoka and Hiroshi Okumura, “Method for Disaster Area Detection with Just One SAR Data Acquired on the Day After Earthquake Based on YOLOv8” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150344

@article{Arai2024,
title = {Method for Disaster Area Detection with Just One SAR Data Acquired on the Day After Earthquake Based on YOLOv8},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150344},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150344},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Kohei Arai and Yushin Nakaoka and Hiroshi Okumura}
}