Machine learning based building damage mapping from the ALOS-2/PALSAR-2 SAR imagery: Case study of 2016 kumamoto earthquake

Yanbing Bai, Bruno Adriano, Erick Mas, Shunichi Koshimura

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)646-655
Number of pages10
JournalJournal of Disaster Research
Volume12
Issue numberSpecial Issue
DOIs
Publication statusPublished - 2017 Jun

Keywords

  • 2016 Kumamoto earthquake
  • ALOS-2/PALSAR-2
  • Building damage mapping
  • Machine learning
  • Synthetic aperture radar

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Engineering (miscellaneous)

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