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dc.contributor.authorCooner, Austin Jeffreyen
dc.date.accessioned2016-12-20T09:00:26Zen
dc.date.available2016-12-20T09:00:26Zen
dc.date.issued2016-12-19en
dc.identifier.othervt_gsexam:9338en
dc.identifier.urihttp://hdl.handle.net/10919/73741en
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.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/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.typeThesisen
dc.contributor.departmentGeographyen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineGeographyen
dc.contributor.committeechairShao, Yangen
dc.contributor.committeememberCampbell, James B. Jr.en
dc.contributor.committeememberPrisley, Stephen P.en


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