Feasibility study of urban flood mapping using traffic signs for route optimization release_uo4mpt7ck5efhl5pu524yaswli

by Bahareh Alizadeh, Diya Li, Zhe Zhang, Amir H. Behzadan

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Water events are the most frequent and costliest climate disasters around the world. In the U.S., an estimated 127 million people who live in coastal areas are at risk of substantial home damage from hurricanes or flooding. In flood emergency management, timely and effective spatial decision-making and intelligent routing depend on flood depth information at a fine spatiotemporal scale. In this paper, crowdsourcing is utilized to collect photos of submerged stop signs, and pair each photo with a pre-flood photo taken at the same location. Each photo pair is then analyzed using deep neural network and image processing to estimate the depth of floodwater in the location of the photo. Generated point-by-point depth data is converted to a flood inundation map and used by an A* search algorithm to determine an optimal flood-free path connecting points of interest. Results provide crucial information to rescue teams and evacuees by enabling effective wayfinding during flooding events.
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Type  article
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Date   2021-09-24
Version   v1
Language   en ?
arXiv  2109.11712v1
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