Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping

• URL: http://arxiv.org/abs/2209.09200v1
• Date: Tue, 6 Sep 2022 18:16:12 GMT
• Title: Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping
• Authors: Bahareh Alizadeh, Amir H. Behzadan
• Abstract summary: In this paper, a deep convolutional network is used to determine flood depth with high spatial resolution by analyzing crowdsourced images of submerged traffic signs. Testing the model on photos from a recent flood in the U.S. and Canada yields a mean absolute error of 6.978 in., which is on par with previous studies.
• Score: 1.6244541005112747
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Successful flood recovery and evacuation require access to reliable flood depth information. Most existing flood mapping tools do not provide real-time flood maps of inundated streets in and around residential areas. In this paper, a deep convolutional network is used to determine flood depth with high spatial resolution by analyzing crowdsourced images of submerged traffic signs. Testing the model on photos from a recent flood in the U.S. and Canada yields a mean absolute error of 6.978 in., which is on par with previous studies, thus demonstrating the applicability of this approach to low-cost, accurate, and real-time flood risk mapping.

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