Crowdsourced-based deep convolutional networks for urban flood depth mapping
B Alizadeh, AH Behzadan - arXiv preprint arXiv:2209.09200, 2022 - arxiv.org
arXiv preprint arXiv:2209.09200, 2022•arxiv.org
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 US and
Canada yields a mean absolute error of 6.978 in., which is on par with previous studies, thus …
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 US and
Canada yields a mean absolute error of 6.978 in., which is on par with previous studies, thus …
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.
arxiv.org
以上显示的是最相近的搜索结果。 查看全部搜索结果