mlsanityData to 7 Oct 2026

Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping

arXiv 2209.09200 ↗First posted 6 Sep 2022cs.CV cs.AI cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 6 Sep 2022.

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References · 18

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6 more
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