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Remote Sens. 2016, 8(10), 868; doi:10.3390/rs8100868

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

Virginia Tech Department of Geography, 115 Major Williams Hall 220 Stanger St., Blacksburg, VA 24060, USA
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Author to whom correspondence should be addressed.
Academic Editors: Roberto Tomas, Zhenhong Li and Prasad S. Thenkabail
Received: 5 August 2016 / Revised: 26 September 2016 / Accepted: 17 October 2016 / Published: 20 October 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
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Abstract

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. View Full-Text
Keywords: earthquake damage; machine learning; computer vision; Random Forests; neural networks earthquake damage; machine learning; computer vision; Random Forests; neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cooner, 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.

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