Proposal of a flood damage road detection method based on deep learning and elevation data
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...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2375545 |
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| Summary: | 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 F-score 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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| ISSN: | 1947-5705 1947-5713 |