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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