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Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data

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Smart City Research Center, School of Automation, Hangzhou Dianzi University, Hangzhou 310012, China
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State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
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State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
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Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1014; https://doi.org/10.3390/rs12061014
Received: 30 January 2020 / Revised: 10 March 2020 / Accepted: 19 March 2020 / Published: 22 March 2020
(This article belongs to the Special Issue Deep Learning Approaches for Urban Sensing Data Analytics)
Reference objects in video images can be used to indicate urban waterlogging depths. The detection of reference objects is the key step to obtain waterlogging depths from video images. Object detection models with convolutional neural networks (CNNs) have been utilized to detect reference objects. These models require a large number of labeled images as the training data to ensure the applicability at a city scale. However, it is hard to collect a sufficient number of urban flooding images containing valuable reference objects, and manually labeling images is time-consuming and expensive. To solve the problem, we present a method to synthesize image data as the training data. Firstly, original images containing reference objects and original images with water surfaces are collected from open data sources, and reference objects and water surfaces are cropped from these original images. Secondly, the reference objects and water surfaces are further enriched via data augmentation techniques to ensure the diversity. Finally, the enriched reference objects and water surfaces are combined to generate a synthetic image dataset with annotations. The synthetic image dataset is further used for training an object detection model with CNN. The waterlogging depths are calculated based on the reference objects detected by the trained model. A real video dataset and an artificial image dataset are used to evaluate the effectiveness of the proposed method. The results show that the detection model trained using the synthetic image dataset can effectively detect reference objects from images, and it can achieve acceptable accuracies of waterlogging depths based on the detected reference objects. The proposed method has the potential to monitor waterlogging depths at a city scale. View Full-Text
Keywords: urban flooding; waterlogging depth; video image; synthetic image data; reference object detection; convolutional neural network urban flooding; waterlogging depth; video image; synthetic image data; reference object detection; convolutional neural network
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MDPI and ACS Style

Jiang, J.; Qin, C.-Z.; Yu, J.; Cheng, C.; Liu, J.; Huang, J. Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data. Remote Sens. 2020, 12, 1014.

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