Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping

Material type: 文書・図像類

Author: ADRIANO, Brunoほか

Publisher: 千葉大学環境リモートセンシング研究センター

Publication date: 2021-02-18

Material Format: Digital

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Note (General)：: type:text[Abstract] We introduce a damage mapping framework for the classification of building damage from space using modern deep learning algorithms...

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Material Type: 文書・図像類
Title: Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
Author/Editor: ADRIANO, Bruno
YOKOYA, Naoto
XIA, Junshi
MIURA, Hiroyuki
LIU, Wen
MATSUOKA, Masashi
KOSHIMURA, Shunichi
Author Heading: ADRIANO, Bruno
YOKOYA, Naoto
XIA, Junshi
MIURA, Hiroyuki
LIU, Wen
MATSUOKA, Masashi
KOSHIMURA, Shunichi
Publication, Distribution, etc.: 千葉大学環境リモートセンシング研究センター
Publication Date: 2021-02-18
Publication Date (W3CDTF): 2021-02-18
Periodical title: CEReS環境リモートセンシングシンポジウム資料集
No. or year of volume/issue: 23
Volume: 23
Pages: 26-26
Text Language Code: eng
Target Audience: 一般
Note (General): type:text
[Abstract] We introduce a damage mapping framework for the classification of building damage from space using modern deep learning algorithms. The main contribution of this work is threefold. (ⅰ) We construct a unique global multitemporal and multimodal EO dataset together with labeled building footprints from large-scale earthquake and tsunami events worldwide. (ⅱ) We propose a damage mapping framework that integrates remote sensing and deep learning to classify the level of building damage considering several data availability scenarios. (ⅲ) We conduct extensive experiments and evaluate the performance of the proposed framework with other state-of-the-art deep learning approaches used for damage recognition.
Access Restrictions: 限定公開
Related Material (NCID): BA64563608
https://ci.nii.ac.jp/ncid/BA64563608
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 千葉大学 : 千葉大学学術成果リポジトリ