{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1390001288085236992.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.20965/jdr.2017.p0646"}},{"identifier":{"@type":"URI","@value":"https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=DSSTR001200070001"}},{"identifier":{"@type":"NAID","@value":"130008128129"}}],"resourceType":"学術雑誌論文(journal article)","dc:title":[{"@language":"en","@value":"Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake"},{"@value":"Machine Learning Based Building Damage Mapping from ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake"}],"dc:language":"en","description":[{"type":"abstract","notation":[{"@language":"en","@value":"<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>"}],"abstractLicenseFlag":"disallow"}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1410001288085236993","@type":"Researcher","personIdentifier":[{"@type":"NRID","@value":"9000414560988"}],"foaf:name":[{"@language":"en","@value":"Bai Yanbing"}],"jpcoar:affiliationName":[{"@language":"en","@value":"Graduate School of Engineering, Tohoku 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Shunichi"}],"jpcoar:affiliationName":[{"@language":"en","@value":"International Research Institute of Disaster Science, Tohoku University"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"18812473"},{"@type":"EISSN","@value":"18838030"}],"prism:publicationName":[{"@language":"ja","@value":"Journal of Disaster Research"},{"@language":"en","@value":"Journal of Disaster Research"},{"@language":"en","@value":"J. Disaster Res."}],"dc:publisher":[{"@language":"en","@value":"Fuji Technology Press Ltd."},{"@language":"ja","@value":"富士技術出版株式会社"}],"prism:publicationDate":"2017-06-30","prism:volume":"12","prism:number":"sp","prism:startingPage":"646","prism:endingPage":"655"},"reviewed":"false","url":[{"@id":"https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=DSSTR001200070001"}],"availableAt":"2017-06-30","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=2016%20Kumamoto%20earthquake","dc:title":"2016 Kumamoto earthquake"},{"@id":"https://cir.nii.ac.jp/all?q=building%20damage%20mapping","dc:title":"building damage mapping"},{"@id":"https://cir.nii.ac.jp/all?q=ALOS-2/PALSAR-2","dc:title":"ALOS-2/PALSAR-2"},{"@id":"https://cir.nii.ac.jp/all?q=synthetic%20aperture%20radar","dc:title":"synthetic aperture radar"},{"@id":"https://cir.nii.ac.jp/all?q=machine%20learning","dc:title":"machine learning"}],"project":[{"@id":"https://cir.nii.ac.jp/crid/1040000781867436416","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"15KK0226"},{"@type":"JGN","@value":"JP15KK0226"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15KK0226/"}],"notation":[{"@language":"ja","@value":"リモートセンシングとソーシャルセンシングの融合による被災地支援策の刷新（国際共同研究強化）"},{"@language":"en","@value":"Enhancing Post-disaster Recovery by Fusion of Sensing and Simulation(Fostering Joint International Research)"}]},{"@id":"https://cir.nii.ac.jp/crid/1040000781893333888","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"16F16055"},{"@type":"JGN","@value":"JP16F16055"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16F16055/"}],"notation":[{"@language":"ja","@value":"リアルタイムシミュレーションとリモートセンシングの融合による南米の津波予測高度化"},{"@language":"en","@value":"Fusion of real-time simulation and remote sensing for tsunami damage estimation to Latin 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