{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:30:34Z","timestamp":1784212234313,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,20]],"date-time":"2016-10-20T00:00:00Z","timestamp":1476921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010916","name":"DigitalGlobe Foundation","doi-asserted-by":"publisher","award":["0054744883"],"award-info":[{"award-number":["0054744883"]}],"id":[{"id":"10.13039\/100010916","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote 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.<\/jats:p>","DOI":"10.3390\/rs8100868","type":"journal-article","created":{"date-parts":[[2016,10,20]],"date-time":"2016-10-20T10:15:49Z","timestamp":1476958549000},"page":"868","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":191,"title":["Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8019-4455","authenticated-orcid":false,"given":"Austin","family":"Cooner","sequence":"first","affiliation":[{"name":"Virginia Tech Department of Geography, 115 Major Williams Hall 220 Stanger St., Blacksburg, VA 24060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Shao","sequence":"additional","affiliation":[{"name":"Virginia Tech Department of Geography, 115 Major Williams Hall 220 Stanger St., Blacksburg, VA 24060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2617-7272","authenticated-orcid":false,"given":"James","family":"Campbell","sequence":"additional","affiliation":[{"name":"Virginia Tech Department of Geography, 115 Major Williams Hall 220 Stanger St., Blacksburg, VA 24060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2013.06.011","article-title":"A Comprehensive review of earthquake-induced building damage detection with remote sensing techniques","volume":"84","author":"Dong","year":"2013","journal-title":"ISPRS J. 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