  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.
