語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Damage Assessment of Civil Structure...
~
Illinois Institute of Technology.
Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.
作者:
Jones, Scott F.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, 2019
面頁冊數:
81 p.
附註:
Source: Masters Abstracts International, Volume: 81-02.
附註:
Advisor: Saniie, Jafar.
Contained By:
Masters Abstracts International81-02.
標題:
Electrical engineering.
電子資源:
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.
LDR
:02390nmm a2200313 4500
001
570763
005
20200514111954.5
008
200901s2019 ||||||||||||||||| ||eng d
020
$a
9781085589451
035
$a
(MiAaPQ)AAI13877577
035
$a
AAI13877577
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Jones, Scott F.
$3
857453
245
1 0
$a
Damage Assessment of Civil Structures after Natural Disasters Using Deep Learning and Satellite Imagery.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2019
300
$a
81 p.
500
$a
Source: Masters Abstracts International, Volume: 81-02.
500
$a
Advisor: Saniie, Jafar.
502
$a
Thesis (M.S.)--Illinois Institute of Technology, 2019.
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.
590
$a
School code: 0091.
650
4
$a
Electrical engineering.
$3
454503
650
4
$a
Artificial intelligence.
$3
194058
690
$a
0544
690
$a
0800
710
2
$a
Illinois Institute of Technology.
$b
Electrical and Computer Engineering.
$3
660342
773
0
$t
Masters Abstracts International
$g
81-02.
790
$a
0091
791
$a
M.S.
792
$a
2019
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13877577
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000178137
電子館藏
1圖書
學位論文
TH 2019
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13877577
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入