Disaster area detection based on YOLOv8 using SAR data

Yushin Nakaoka; Kohei Arai; Hiroshi Okumura; Osamu Fukuda; Nobuhiko Yamaguchi; Wen Liang Yeoh

doi:10.1117/12.3031749

19 November 2024 Disaster area detection based on YOLOv8 using SAR data

Yushin Nakaoka, Kohei Arai, Hiroshi Okumura, Osamu Fukuda, Nobuhiko Yamaguchi, Wen Liang Yeoh

Author Affiliations +

Proceedings Volume 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX; 131960H (2024) https://doi.org/10.1117/12.3031749
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Various natural disasters occur on the earth. In Japan, heavy rains and earthquakes have caused particularly severe damage. We focus on landslides caused by them. This study proposes a landslide detection method using synthetic aperture radar (SAR). SAR uses microwave observations, and microwaves are reflected according to the properties of materials on the earth’s surface. In addition, microwave amplitude and phase information can be obtained, and these are used for various analyses. They are often used to detect disasters, mostly to detect changes caused by disasters. For example, change detection by differential reflection intensity, analysis of terrain variation by phase difference, and detection of material by properties of polarization. Therefore, multiple SAR data are required for disaster detection. However, in the event of a disaster, rapid detection of the damaged area is necessary. For this reason, this study investigates a method for detecting the damaged area from a single SAR data. As a research method, instance segmentation is conducted using YOLOv8. The SAR data used in the experiments were obtained for the Noto Peninsula earthquake. This disaster occurred on January 1st in 2024 in the Noto region of Ishikawa Prefecture and caused extensive damage. Images of landslide areas were obtained from SAR data, annotated and trained instance segmentation by YOLOv8 to evaluate test performance.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yushin Nakaoka, Kohei Arai, Hiroshi Okumura, Osamu Fukuda, Nobuhiko Yamaguchi, and Wen Liang Yeoh "Disaster area detection based on YOLOv8 using SAR data", Proc. SPIE 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX, 131960H (19 November 2024); https://doi.org/10.1117/12.3031749

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