Geomatics, Natural Hazards & Risk (Dec 2024)

Proposal of a flood damage road detection method based on deep learning and elevation data

  • Jun Sakamoto

DOI
https://doi.org/10.1080/19475705.2024.2375545
Journal volume & issue
Vol. 15, no. 1

Abstract

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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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