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Open AccessArticle

A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network

by 1,2, 1,2,*, 1,2, 1,2 and 2
1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2
Institute of Management Science, Business School, Hohai University, Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(5), 2149; https://doi.org/10.3390/su12052149
Received: 15 January 2020 / Revised: 7 March 2020 / Accepted: 9 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Water Resources and Green Growth)
Quickly obtaining accurate waterlogging depth data is vital in urban flood events, especially for emergency response and risk mitigation. In this study, a novel approach to measure urban waterlogging depth was developed using images from social networks and traffic surveillance video systems. The Mask region-based convolutional neural network (Mask R-CNN) model was used to detect tires in waterlogging, which were considered to be reference objects. Then, waterlogging depth was calculated using the height differences method and Pythagorean theorem. The results show that tires detected from images can been used as an effective reference object to calculate waterlogging depth. The Pythagorean theorem method performs better on images from social networks, and the height differences method performs well both on the images from social networks and on traffic surveillance video systems. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing. View Full-Text
Keywords: urban waterlogging depth; vehicle tires; image detection; Mask R-CNN urban waterlogging depth; vehicle tires; image detection; Mask R-CNN
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MDPI and ACS Style

Huang, J.; Kang, J.; Wang, H.; Wang, Z.; Qiu, T. A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network. Sustainability 2020, 12, 2149.

AMA Style

Huang J, Kang J, Wang H, Wang Z, Qiu T. A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network. Sustainability. 2020; 12(5):2149.

Chicago/Turabian Style

Huang, Jing; Kang, Jinle; Wang, Huimin; Wang, Zhiqiang; Qiu, Tian. 2020. "A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network" Sustainability 12, no. 5: 2149.

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