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Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

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dc.contributor.authorCooner, Austin J.en
dc.contributor.authorShao, Yangen
dc.contributor.authorCampbell, James B. Jr.en
dc.contributor.departmentGeographyen
dc.date.accessioned2017-03-28T17:31:55Zen
dc.date.available2017-03-28T17:31:55Zen
dc.date.issued2016-10-20en
dc.description.abstractRemote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCooner, A.J.; Shao, Y.; Campbell, J.B. Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake. Remote Sens. 2016, 8, 868.en
dc.identifier.doihttps://doi.org/10.3390/rs8100868en
dc.identifier.issue10en
dc.identifier.urihttp://hdl.handle.net/10919/76693en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectearthquake damageen
dc.subjectMachine learningen
dc.subjectcomputer visionen
dc.subjectRandom Forestsen
dc.subjectneural networksen
dc.titleDetection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquakeen
dc.title.serialRemote Sensingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)
Scholarly Works, Geography