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dc.contributor.authorGazzea, Michele
dc.contributor.authorKaraer, Alican
dc.contributor.authorGhorbanzadeh, Mahyar
dc.contributor.authorBalafkan, Nozhan
dc.contributor.authorAbichou, Tarek
dc.contributor.authorOzguven, Eren Erman
dc.contributor.authorArghandeh, Reza
dc.date.accessioned2021-11-09T13:31:08Z
dc.date.available2021-11-09T13:31:08Z
dc.date.created2021-09-09T00:40:52Z
dc.date.issued2021
dc.identifier.citationGazzea, M., Karaer, A., Ghorbanzadeh, M., Balafkan, N., Abichou, T., Ozguven, E. E., & Arghandeh, R. (2021). Automated satellite-based assessment of hurricane impacts on roadways. IEEE Transactions on Industrial Informaticsen_US
dc.identifier.issn1551-3203
dc.identifier.urihttps://hdl.handle.net/11250/2828703
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.description.abstractDuring extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a co-voting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 2018en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.subjectremote sensingen_US
dc.subjectsatellite imageryen_US
dc.subjecttree debris detectionen_US
dc.subjectpost-hurricane assessmenten_US
dc.subjectdeep-learningen_US
dc.titleAutomated Satellite-based Assessment of Hurricane Impacts on Roadwaysen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2021 IEEEen_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.source.journalIEEE Transactions on Industrial Informaticsen_US
dc.identifier.doi10.1109/TII.2021.3082906
dc.identifier.cristin1932626
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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