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Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning

1
Smart City Research Center, Hangzhou Dianzi University, Hangzhou 310012, China
2
Smart City Collaborative Innovation Center of Zhejiang Province, Hangzhou 310012, China
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Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
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Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
6
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(10), 1485; https://doi.org/10.3390/w10101485
Received: 25 September 2018 / Revised: 18 October 2018 / Accepted: 19 October 2018 / Published: 21 October 2018
(This article belongs to the Special Issue Flash Floods in Urban Areas)
Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in video form. These video data could be a valuable data source for waterlogging depth extraction. The present paper is aimed at demonstrating a new approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. First, a transfer learning model is used to extract feature vectors from a video image set of urban waterlogging. Second, a lasso regression model is trained with these feature vectors and employed to calculate the waterlogging depth. Two case studies in China were used to evaluate the proposed method, and the experimental results illustrate the effectiveness of the method. This method can be applied to video images from widespread cameras in cities, so that a powerful urban waterlogging monitoring network can be formed. View Full-Text
Keywords: urban waterlogging depth; video image; transfer learning; lasso regression urban waterlogging depth; video image; transfer learning; lasso regression
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MDPI and ACS Style

Jiang, J.; Liu, J.; Qin, C.-Z.; Wang, D. Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning. Water 2018, 10, 1485. https://doi.org/10.3390/w10101485

AMA Style

Jiang J, Liu J, Qin C-Z, Wang D. Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning. Water. 2018; 10(10):1485. https://doi.org/10.3390/w10101485

Chicago/Turabian Style

Jiang, Jingchao; Liu, Junzhi; Qin, Cheng-Zhi; Wang, Dongliang. 2018. "Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning" Water 10, no. 10: 1485. https://doi.org/10.3390/w10101485

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