Estimating the spatial-temporal distribution of urban street ponding levels from surveillance videos based on computer vision
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DOI: 10.1007/s11269-022-03107-2
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- J. F. Rosser & D. G. Leibovici & M. J. Jackson, 2017. "Rapid flood inundation mapping using social media, remote sensing and topographic data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 103-120, May.
- Alireza Arabameri & Aman Arora & Subodh Chandra Pal & Satarupa Mitra & Asish Saha & Omid Asadi Nalivan & Somayeh Panahi & Hossein Moayedi, 2021. "K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1837-1869, April.
- Suresh Kumar Sharma & A. Seetharaman & K. Maddulety, 2021. "Framework for Sustainable Urban Water Management in Context of Governance, Infrastructure, Technology and Economics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3903-3913, September.
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Keywords
Urban flood; Ponding level distribution; Computer vision; Object detection; Surveillance video;All these keywords.
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