Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events

Bruno Adriano; Junshi Xia; Naoto Yokoya; Hiroyuki Miura; Masashi Matsuoka; Shunichi Koshimura

doi:10.1109/IGARSS39084.2020.9323415

Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events

Bruno Adriano, Junshi Xia, Naoto Yokoya, Hiroyuki Miura, Masashi Matsuoka, Shunichi Koshimura

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Disasters such as earthquakes, hurricanes, and flooding are responsible for large-scale infrastructure damages and loss of human lives. Immediately after disaster strikes, one of the most critical and difficult tasks is accurately assessing the extent and severity of the disaster. This task is especially challenging in areas isolated by the disaster; in such cases, remote sensing information provides the best alternative to tackle this problem. This paper presents a damage mapping framework using remote sensing imagery acquired from previous disasters. The proposed deep learning-based framework is trained to learn features related to building damage using imagery from previous disasters that were collected from different regions around the world. Then, it is tested to recognize damage from a different urban environment.

本文言語	English
ホスト出版物のタイトル	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
出版社	Institute of Electrical and Electronics Engineers Inc.
ページ	3751-3754
ページ数	4
ISBN（電子版）	9781728163741
DOI	https://doi.org/10.1109/IGARSS39084.2020.9323415
出版ステータス	Published - 2020 9月 26
イベント	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States 継続期間: 2020 9月 26 → 2020 10月 2

出版物シリーズ

名前	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
国/地域	United States
City	Virtual, Waikoloa
Period	20/9/26 → 20/10/2

ASJC Scopus subject areas

コンピュータサイエンスの応用
地球惑星科学（全般）

UN SDG

この成果は、次の持続可能な開発目標に貢献しています

文献へのアクセス

10.1109/IGARSS39084.2020.9323415

他のファイルとリンク

引用スタイル

Adriano, B., Xia, J., Yokoya, N., Miura, H., Matsuoka, M., & Koshimura, S. (2020). Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 3751-3754). [9323415] (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9323415

Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events. / Adriano, Bruno; Xia, Junshi; Yokoya, Naoto その他.

2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 3751-3754 9323415 (International Geoscience and Remote Sensing Symposium (IGARSS)).

研究成果: Conference contribution

Adriano, B, Xia, J, Yokoya, N, Miura, H, Matsuoka, M & Koshimura, S 2020, Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9323415, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 3751-3754, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 20/9/26. https://doi.org/10.1109/IGARSS39084.2020.9323415

Adriano B, Xia J, Yokoya N, Miura H, Matsuoka M, Koshimura S. Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 3751-3754. 9323415. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9323415

Adriano, Bruno ; Xia, Junshi ; Yokoya, Naoto その他. / Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 3751-3754 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{ff126eaf4cd544e5a25b6816518125dd,

title = "Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events",

abstract = "Disasters such as earthquakes, hurricanes, and flooding are responsible for large-scale infrastructure damages and loss of human lives. Immediately after disaster strikes, one of the most critical and difficult tasks is accurately assessing the extent and severity of the disaster. This task is especially challenging in areas isolated by the disaster; in such cases, remote sensing information provides the best alternative to tackle this problem. This paper presents a damage mapping framework using remote sensing imagery acquired from previous disasters. The proposed deep learning-based framework is trained to learn features related to building damage using imagery from previous disasters that were collected from different regions around the world. Then, it is tested to recognize damage from a different urban environment.",

keywords = "Damage Mapping, Deep Learning, Multitemporal, Tsunami-induced damage",

author = "Bruno Adriano and Junshi Xia and Naoto Yokoya and Hiroyuki Miura and Masashi Matsuoka and Shunichi Koshimura",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",

year = "2020",

month = sep,

day = "26",

doi = "10.1109/IGARSS39084.2020.9323415",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3751--3754",

booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",

}

TY - GEN

T1 - Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events

AU - Adriano, Bruno

AU - Xia, Junshi

AU - Yokoya, Naoto

AU - Miura, Hiroyuki

AU - Matsuoka, Masashi

AU - Koshimura, Shunichi

PY - 2020/9/26

Y1 - 2020/9/26

N2 - Disasters such as earthquakes, hurricanes, and flooding are responsible for large-scale infrastructure damages and loss of human lives. Immediately after disaster strikes, one of the most critical and difficult tasks is accurately assessing the extent and severity of the disaster. This task is especially challenging in areas isolated by the disaster; in such cases, remote sensing information provides the best alternative to tackle this problem. This paper presents a damage mapping framework using remote sensing imagery acquired from previous disasters. The proposed deep learning-based framework is trained to learn features related to building damage using imagery from previous disasters that were collected from different regions around the world. Then, it is tested to recognize damage from a different urban environment.

AB - Disasters such as earthquakes, hurricanes, and flooding are responsible for large-scale infrastructure damages and loss of human lives. Immediately after disaster strikes, one of the most critical and difficult tasks is accurately assessing the extent and severity of the disaster. This task is especially challenging in areas isolated by the disaster; in such cases, remote sensing information provides the best alternative to tackle this problem. This paper presents a damage mapping framework using remote sensing imagery acquired from previous disasters. The proposed deep learning-based framework is trained to learn features related to building damage using imagery from previous disasters that were collected from different regions around the world. Then, it is tested to recognize damage from a different urban environment.

KW - Damage Mapping

KW - Deep Learning

KW - Multitemporal

KW - Tsunami-induced damage

UR - http://www.scopus.com/inward/record.url?scp=85101965349&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85101965349&partnerID=8YFLogxK

U2 - 10.1109/IGARSS39084.2020.9323415

DO - 10.1109/IGARSS39084.2020.9323415

M3 - Conference contribution

AN - SCOPUS:85101965349

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 3751

EP - 3754

BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020

Y2 - 26 September 2020 through 2 October 2020

ER -

Damage Characterization in Urban Environments from Multitemporal Remote Sensing Datasets Built from Previous Events

抄録

出版物シリーズ

Conference

ASJC Scopus subject areas

UN SDG

文献へのアクセス

他のファイルとリンク

フィンガープリント

引用スタイル