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

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

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-Prin...

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Bibliographic Details
Main Authors: Austin J. Cooner, Yang Shao, James B. Campbell
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:Remote Sensing
Subjects:
earthquake damage
machine learning
computer vision
Random Forests
neural networks
Online Access:http://www.mdpi.com/2072-4292/8/10/868

Internet

http://www.mdpi.com/2072-4292/8/10/868

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