Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake
| Auteur(s): |
Yanbing Bai
Bruno Adriano Erick Mas Shunichi Koshimura |
|---|---|
| Médium: | article de revue |
| Langue(s): | anglais |
| Publié dans: | Journal of Disaster Research, 2017, v. 12 |
| Page(s): | 646-655 |
| DOI: | 10.20965/jdr.2017.p0646 |
| Abstrait: | 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. |
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sur cette fiche - Reference-ID
10684932 - Publié(e) le:
13.08.2022 - Modifié(e) le:
20.08.2022
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