Proceedings of the 2022 European Conference on Computing in Construction


Crowdsourced-based deep convolutional networks for urban flood depth mapping

Bahareh Alizadeh, Amir Behzadan
Texas A&M University, United States of America

DOI: 10.35490/EC3.2022.145
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.
Keywords: Artificial intelligence, Crowdsourcing, Flood mapping, Climate change, Urban resilience

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