WEKO3
アイテム
Proposal of A Flood Damage Road Detection Method Based on Deep Learning and Elevation Data
http://hdl.handle.net/10126/0002000673
http://hdl.handle.net/10126/000200067356274295-0e4c-4d7a-beb9-af1336728bf5
| 名前 / ファイル | ライセンス | アクション |
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Proposal_of_a_flood_damage_road_detection_method_based_on_deep_learning_and_elevation_data.pdf (4.8 MB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
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| 公開日 | 2026-03-20 | |||||||
| タイトル | ||||||||
| タイトル | Proposal of A Flood Damage Road Detection Method Based on Deep Learning and Elevation Data | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | Deep learning | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | YOLOv3 | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | disrupted section | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | aerial photograph | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | GIS | |||||||
| キーワード | ||||||||
| 言語 | en | |||||||
| 主題Scheme | Other | |||||||
| 主題 | fundamental geospatial data | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||
| 資源タイプ | journal article | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| 著者 |
坂本 淳
× 坂本 淳
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| 著者別名 | ||||||||
| 姓名 | Sakamoto Jun | |||||||
| 言語 | en | |||||||
| 抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Identifying an inundation area after a flood event is essential for planning emergency rescue operations. In this study, we propose a method to automatically determine inundated road segments by floods using image recognition technology, a deep learning model, and elevation data. First, we develop a training model using aerial photographs captured during a flood event. Then, the model is applied to aerial photographs captured during another flood event. The model visualizes the inundation status of roads on a 100-m mesh-by-mesh basis using aerial photographs and integrating the information on whether the mesh includes targeted road segments. Our results showed that the Fscore was higher, 89%–91%, when we targeted only road segments with 15 m or less. Moreover, visualizing in GIS facilitated the classification of inundated roads, even within the same 100-m mesh, which is a relevant finding that complements deep learning object detection |
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| 言語 | en | |||||||
| 書誌情報 |
en : Geomatics, Natural Hazards and Risk 巻 15, 号 1, p. 2375545, 発行日 2024-07-09 |
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| ISSN | ||||||||
| 収録物識別子タイプ | PISSN | |||||||
| 収録物識別子 | 1947-5705 | |||||||
| DOI | ||||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1080/19475705.2024.2375545 | |||||||
| 権利 | ||||||||
| 権利情報 | 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. | |||||||
| 言語 | en | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| 出版者 | ||||||||
| 出版者 | Taylor&Francis | |||||||
| 言語 | en | |||||||
