Detecting Demolished Buildings after a Natural Hazard Using High Resolution RGB Satellite Imagery and Modified U-Net Convolutional Neural Networks

Detecting Demolished Buildings after a Natural Hazard Using High Resolution RGB Satellite Imagery and Modified U-Net Convolutional Neural Networks

Collapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery...

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Bibliographic Details
Main Authors: Vahid Rashidian, Laurie G. Baise, Magaly Koch, Babak Moaveni
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Remote Sensing
Subjects:
earthquake
building damage detection
remote sensing
rapid response
satellite imagery
rescue efforts
Online Access:https://www.mdpi.com/2072-4292/13/11/2176

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https://www.mdpi.com/2072-4292/13/11/2176

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