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Damage Assessment of Civil Structure...

Illinois Institute of Technology.

Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.
Author:	Jones, Scott F.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2019
Description:	81 p.
Notes:	Source: Masters Abstracts International, Volume: 81-02.
Notes:	Advisor: Saniie, Jafar.
Contained By:	Masters Abstracts International81-02.
Subject:	Electrical engineering.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13877577
ISBN:	9781085589451

Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.
Jones, Scott F.

Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 81 p.

Source: Masters Abstracts International, Volume: 81-02.

Thesis (M.S.)--Illinois Institute of Technology, 2019.

This item must not be sold to any third party vendors.

Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster. The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.

ISBN: 9781085589451Subjects--Topical Terms:

454503
Electrical engineering.

Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.
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500 $a Source: Masters Abstracts International, Volume: 81-02.
500 $a Advisor: Saniie, Jafar.
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506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster. The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.
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