A Machine Learning Approach to Flood Depth and Extent Detection Using Sentinel 1A/B Synthetic Aperture Radar

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dc.contributor.authorTiampo, K.en
dc.contributor.authorWoods, C.en
dc.contributor.authorHuang, L.en
dc.contributor.authorSharma, P.en
dc.contributor.authorChen, Z.en
dc.contributor.authorKar, B.en
dc.contributor.authorBausch, D.en
dc.contributor.authorSimmons, C.en
dc.contributor.authorEstrada, R.en
dc.contributor.authorWillis, Michael J.en
dc.contributor.authorGlasscoe, M.en
dc.date.accessioned2024-02-21T19:42:54Zen
dc.date.available2024-02-21T19:42:54Zen
dc.date.issued2021-01-01en
dc.description.abstractThe rising number of flooding events combined with increased urbanization is contributing to significant economic losses due to damages to structures and infrastructures. Here we present a method for producing all weather maps of flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods that can be used to provide information on the evolution of flood hazards to DisasterAware©, a global alerting system, that is used to disseminate flood risk information to stakeholders across the globe. While these efforts are still in development, a case study is presented for the major flood event associated with Hurricane Harvey and associated floods that impacted Houston, TX in August of 2017.en
dc.description.versionPublished versionen
dc.format.extentPages 558-561en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/IGARSS47720.2021.9553601en
dc.identifier.isbn9781665403696en
dc.identifier.urihttps://hdl.handle.net/10919/118097en
dc.identifier.volume2021-Julyen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectSynthetic aperture radaren
dc.subjectFlood characterizationen
dc.subjectFlood inundationen
dc.subjectMachine learningen
dc.subjectGeospatial data fusionen
dc.titleA Machine Learning Approach to Flood Depth and Extent Detection Using Sentinel 1A/B Synthetic Aperture Radaren
dc.title.serialInternational Geoscience and Remote Sensing Symposium (IGARSS)en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.finish-date2021-07-16en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Geosciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.start-date2021-07-11en

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