Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping

Autor:	Alizadeh, Bahareh, Behzadan, Amir H.
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction 94-08 68T45 68T07 I.2.10 I.4.8 I.5
Druh dokumentu:	Working Paper
DOI:	10.35490/EC3.2022.145
Popis:	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. Comment: 2022 European Conference on Computing in Construction
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.09200 Zobrazit plný text záznamu View this record from Arxiv