Machine Learning Based Building Damage Mapping from ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake

DOI Web Site 被引用文献14件 参考文献27件
  • Bai Yanbing
    Graduate School of Engineering, Tohoku University
  • Adriano Bruno
    International Research Institute of Disaster Science, Tohoku University
  • Mas Erick
    International Research Institute of Disaster Science, Tohoku University
  • Koshimura Shunichi
    International Research Institute of Disaster Science, Tohoku University

書誌事項

タイトル別名
  • Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake
公開日
2017-06-30
資源種別
journal article
DOI
  • 10.20965/jdr.2017.p0646
公開者
富士技術出版株式会社

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説明

<p>Synthetic Aperture Radar (SAR) remote sensing is a useful tool for mapping earthquake-induced building damage. A series of operational methodologies based on SAR data using either multi-temporal or only post-event SAR images have been developed and used to serve disaster activities. This presents a critical problem: which method is more likely to obtain reliable results and should be adopted for disaster response when both pre- and post-event SAR data are available? To explore this question, this study takes the 2016 Kumamoto earthquake as a case study. ALOS-2/PALSAR-2 SAR images were employed with a machine learning framework to quantitatively compare the performance of building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images. The results show that an overall accuracy of 64.5% was achieved when only post-event SAR images were used, which is 2.3% higher than the overall accuracy when multi-temporal SAR images were used. The estimated building damage ratio for the former and the latter are 29.7% and 31.1%, respectively, which are both close to the building damage ratio obtained from an optical image.</p>

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