Abstract
Efficient and accurate monitoring urban ponding by surveillance video is of great significance to reduce the risk of inundation and urban traffic. The previous work lacks consideration of real-time performance and integration of a unified management platform, which leads to low monitoring efficiency. This demo presents an urban road ponding monitoring and warning system (URPWS) based on surveillance video. URPWS provides a platform that integrates intelligent monitoring, real-time warning and unified management, which realizes the real-time and manageability of monitoring. In this demo, we bring forth the application of URPWS in Nanning of Guangxi Province, which delivers a new feasible solution for urban road ponding monitoring and management.
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References
Bai, G., et al.: An intelligent water level monitoring method based on ssd algorithm. Measurement 185, 110047 (2021)
Liu, H., Zou, L., Xia, J., Chen, T., Wang, F.: Impact assessment of climate change and urbanization on the nonstationarity of extreme precipitation: a case study in an urban agglomeration in the middle reaches of the yangtze river. Sustain. Urban Areas 85, 104038 (2022)
Muhadi, N.A., Abdullah, A.F., Bejo, S.K., Mahadi, M.R., Mijic, A.: Deep learning semantic segmentation for water level estimation using surveillance camera. Appl. Sci. 11(20), 9691 (2021)
Wang, Y., Li, K., Chen, G., Zhang, Y., Guo, D., Wang, M.: Spatiotemporal contrastive modeling for video moment retrieval. World Wide Web 26, 1525–1544 (2022)
Wu, S., Li, X., Dong, W., Wang, S., Zhang, X., Xu, Z.: Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey. World Wide Web 26(3), 1115–1156 (2023)
Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., Sang, N.: Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vision 129, 3051–3068 (2021)
Acknowledgements
This work was supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (KF-2021-06-088) and the National Natural Science Foundation of China (42071382). The demo data is provided by Nanning Survey and Design Institute Group Co., LTD.
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Xu, R. et al. (2024). URPWS: An Urban Road Ponding Monitoring and Warning System Based on Surveillance Video. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_35
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DOI: https://doi.org/10.1007/978-981-97-2421-5_35
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