Computers and Concrete

  • 제33권4호
  • /
  • Pages.425-443
  • /
  • 2024
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A review of ground camera-based computer vision techniques for flood management

  • Sanghoon Jun (Hyper-converged Forensic Research Center for Infrastructure, Korea University) ;
  • Hyewoon Jang (Department of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Seungjun Kim (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Jong-Sub Lee (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Donghwi Jung (School of Civil, Environmental and Architectural Engineering, Korea University)
  • 투고 : 2023.12.27
  • 심사 : 2024.02.16
  • 발행 : 2024.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A5A1032433), and Korea Environment Industry & Technology Institute(KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis, funded by Korea Ministry of Environment (MOE) (RS2023-00218873).

참고문헌

  1. Al-mamari, M.M., Kantoush, S.A., Kobayashi, S., Sumi, T. and Saber, M. (2019), "Real-time measurement of flash-flood in a wadi area by LSPIV and STIV", Hydrol., 6(1), 27. https://doi.org/10.3390/hydrology6010027.
  2. Ansari, S., Rennie, C.D., Jamieson, E.C., Seidou, O. and Clark, S.P. (2023), "RivQNet: Deep learning based river discharge estimation using close-range water surface imagery", Water Resour. Res., 59(2), e2021WR031841. https://doi.org/10.1029/2021WR031841.
  3. Arshad, B., Ogie, R., Barthelemy, J., Pradhan, B., Verstaevel, N. and Perez, P. (2019), "Computer vision and IoT-based sensors in flood monitoring and mapping: A systematic review", Sensors, 19(22), 5012. https://doi.org/10.3390/s19225012.
  4. Badrinarayanan, V., Kendall, A. and Cipolla, R. (2017), "Segnet: A deep convolutional encoder-decoder architecture for image segmentation", IEEE Trans. Pattern Anal. Mach. Intell., 39(12), 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615.
  5. Bakhtiari, V., Piadeh, F., Behzadian, K. and Kapelan, Z. (2023), "A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management", Sustain. Cities Soc., 99, 104958. https://doi.org/10.1016/j.scs.2023.104958.
  6. Bharti, P., Chandra, P., Papka, M. and Koop, D. (2022), "An edge map based ensemble solution to detect water level in stream", arXiv preprint arXiv:2201.06098. https://doi.org/10.48550/arXiv.2201.06098.
  7. Blanch, X., Wagner, F. and Eltner, A. (2022), RIWA Dataset; Kaggle, San Francisco, CA, USA. https://www.kaggle.com/datasets/franzwagner/river-watersegmentation-dataset/versions/1
  8. Blender Community (2020), Blender - A 3D Modelling and Rendering Package; Blender, Amsterdam, Netherlands. www.blender.org
  9. Bodart, G., Le Coz, J., Jodeau, M. and Hauet, A. (2022), "Synthetic river flow videos for evaluating image-based velocimetry methods", Water Resour. Res., 58(12), e2022WR032251. https://doi.org/10.1029/2022WR032251.
  10. Brookner, E. (1998), Tracking and Kalman Filtering Made Easy, John Wiley & Sons, Inc., Hoboken, NJ, USA.
  11. Canny, J. (1986), "A computational approach to edge detection", IEEE Trans. Pattern Anal. Mach. Intell., PAMI-8(6), 679-698. https://doi.org/10.1109/TPAMI.1986.4767851.
  12. Cao, Y., Wu, Y., Yao, Q., Yu, J., Hou, D., Wu, Z. and Wang, Z. (2022), "River surface velocity estimation using optical flow velocimetry improved with attention mechanism and position encoding", IEEE Sens. J., 22(16), 16533-16544. https://doi.org/10.1109/JSEN.2022.3186972.
  13. Chaudhary, P., D'Aronco, S., Leitao, J.P., Schindler, K. and Wegner, J.D. (2020), "Water level prediction from social media images with a multi-task ranking approach", ISPRS J. Photogramm. Remote Sens., 167, 252-262. https://doi.org/10.1016/j.isprsjprs.2020.07.003.
  14. Chen, C., Fu, R., Ai, X., Huang, C., Cong, L., Li, X., Jiang, J. and Pei, Q. (2022), "An integrated method for river water level recognition from surveillance images using convolution neural networks", Remote Sens., 14(23), 6023. https://doi.org/10.3390/rs14236023.
  15. Detert, M. (2020), "How to avoid and correct biased riverine surface image velocimetry", Water Resour. Res., 57(2), e2020WR027833. https://doi.org/10.1029/2020WR027833.
  16. Dou, G., Chen, R., Han, C., Liu, Z. and Liu, J. (2022), "Research on water-level recognition method based on image processing and convolutional neural networks", Water, 14(12), 1890. https://doi.org/10.3390/w14121890.
  17. Eltner, A., Elias, M., Sardemann, H. and Spieler, D. (2018), "Automatic image-based water stage measurement for long-term observations in ungauged catchments", Water Resour. Res., 54(12), 10362-10371. https://doi.org/10.1029/2018WR023913.
  18. Eltner, A., Bressan, P.O., Akiyama, T., Goncalves, W.N. and Marcato Junior, J. (2021), "Using deep learning for automatic water stage measurements", Water Resour. Res., 57(3), e2020WR027608. https://doi.org/10.1029/2020WR027608.
  19. Etter, S., Strobl, B., van Meerveld, I. and Seibert, J. (2020a), "Quality and timing of crowd-based water level class observations", Hydrol. Process., 34(22), 4365-4378. https://doi.org/10.1002/hyp.13864.
  20. Etter, S., Strobl, B., Seibert, J. and van Meerveld, H.I. (2020), "Value of crowd-based water level class observations for hydrological model calibration", Water Resour. Res., 56(2), e2019WR026108. https://doi.org/10.1029/2019WR026108.
  21. Felzenszwalb, P.F., Girshick, R.B., McAllester, D. and Ramanan, D. (2009), "Object detection with discriminatively trained part-based models", IEEE Trans. Pattern Anal. Mach. Intell., 32(9), 1627-1645. https://doi.org/10.1109/TPAMI.2009.167.
  22. Feng, Y., Brenner, C. and Sester, M. (2020), "Flood severity mapping from volunteered geographic information by interpreting water level from images containing people: A case study of Hurricane Harvey", ISPRS J. Photogramm. Remote Sens., 169, 301-319. https://doi.org/10.1016/j.isprsjprs.2020.09.011.
  23. Fernandes Junior, F.E., Nonato, L.G., Ranieri, C.M. and Ueyama, J. (2021), "Memory-based pruning of deep neural networks for IoT devices applied to flood detection", Sensors, 21(22), 7506. https://doi.org/10.3390/s21227506.
  24. Fujita, I., Muste, M. and Kruger, A. (1998), "Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications", J. Hydraul. Res., 36(3), 397-414. https://doi.org/10.1080/00221689809498626.
  25. Gilmore, T.E., Birgand, F. and Chapman, K.W. (2013), "Source and magnitude of error in an inexpensive image-based water level measurement system", J. Hydrol., 496, 178-186. https://doi.org/10.1016/j.jhydrol.2013.05.011.
  26. Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014), "Rich feature hierarchies for accurate object detection and semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., Columbus, OH, USA, June.
  27. Girshick, R. (2015), "Fast R-CNN", Proceedings of the IEEE International Conference on Computer Vision., Santiago, Chile, December.
  28. Hao, X., Lyu, H., Wang, Z., Fu, S. and Zhang, C. (2022), "Estimating the spatial-temporal distribution of urban street ponding levels from surveillance videos based on computer vision", Water Resour. Manag., 36(6), 1799-1812. https://doi.org/10.1007/s11269-022-03107-2.
  29. He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., Las Vegas, NV, USA, June.
  30. He, K., Gkioxari, G., Dollar, P. and Girshick, R. (2017), "Mask R-CNN", Proceedings of the IEEE International Conference on Computer Vision., Venice, Italy, October.
  31. Hiroi, K. and Kawaguchi, N. (2016), "FloodEye: Real-time flash flood prediction system for urban complex water flow", 2016 IEEE Sensors, Orlando, FL, USA, October-November.
  32. Horn, B.K. and Schunck, B.G. (1981), "Determining optical flow", Artif. Intell., 17(1-3), 185-203. https://doi.org/10.1016/0004-3702(81)90024-2.
  33. Hou, J., Li, X., Bai, G., Wang, X., Zhang, Z., Yang, L., Du, Y., Ma, Y. and Zhang, X. (2021), "A deep learning technique based flood propagation experiment", J. Flood Risk Manag., 14(3), e12718. https://doi.org/10.1111/jfr3.12718.
  34. Hou, J., Yang, L., Wang, X., Chai, J., Zhang, Z., Li, X., Shao, J., Du, Y. and Bai, G. (2022), "Adaptive large-scale particle image velocimetry method for physical model experiments of flood propagation with complex flow patterns", Measure., 198, 111309. https://doi.org/10.1016/j.measurement.2022.111309.
  35. Hsu, S.Y., Chen, T.B., Du, W.C., Wu, J.H. and Chen, S.C. (2019), "Integrate weather radar and monitoring devices for urban flooding surveillance", Sensors, 19(4), 825. https://doi.org/10.3390/s19040825.
  36. Huang, J., Kang, J., Wang, H., Wang, Z. and Qiu, T. (2020), "A novel approach to measuring urban waterlogging depth from images based on mask region-based convolutional neural network", Sustainab., 12(5), 2149. https://doi.org/10.3390/su12052149.
  37. Huang, H., Lei, X., Liao, W., Li, H., Wang, C. and Wang, H. (2023), "A real-time detecting method for continuous urban flood scenarios based on computer vision on block scale", Remote Sens., 15(6), 1696. https://doi.org/10.3390/rs15061696.
  38. Hutley, N.R., Beecroft, R., Wagenaar, D., Soutar, J., Edwards, B., Deering, N., Grinham, A. and Albert, S. (2023), "Adaptively monitoring streamflow using a stereo computer vision system", Hydrol. Earth System Sci., 27(10), 2051-2073. https://doi.org/10.5194/hess-27-2051-2023.
  39. Iqbal, U., Perez, P., Li, W. and Barthelemy, J. (2021), "How computer vision can facilitate flood management: A systematic review", Int. J. Disaster Risk Reduction, 53, 102030. https://doi.org/10.1016/j.ijdrr.2020.102030.
  40. Iqbal, U., Riaz, M.Z. B., Zhao, J., Barthelemy, J. and Perez, P. (2023), "Drones for flood monitoring, mapping and detection: A bibliometric review", Drones, 7(1), 32. https://doi.org/10.3390/drones7010032.
  41. Isidoro, J.M., Martins, R., Carvalho, R.F. and de Lima, J.L. (2021), "A high-frequency low-cost technique for measuring small-scale water level fluctuations using computer vision", Measure., 180, 109477. https://doi.org/10.1016/j.measurement.2021.109477.
  42. Jafari, N.H., Li, X., Chen, Q., Le, C.Y., Betzer, L.P. and Liang, Y. (2021), "Real-time water level monitoring using live cameras and computer vision techniques", Comput. Geosci., 147, 104642. https://doi.org/10.1016/j.cageo.2020.104642.
  43. Kamoji, S. and Kalla, M. (2023), "Effective flood prediction model based on twitter text and image analysis using BMLP and SDAE-HHNN", Eng. Appl. Artif. Intell., 123, 106365. https://doi.org/10.1016/j.engappai.2023.106365.
  44. Kantoush, S.A., Schleiss, A.J., Sumi, T. and Murasaki, M. (2011), "LSPIV implementation for environmental flow in various laboratory and field cases", J. Hydro-environ. Res., 5(4), 263-276. https://doi.org/10.1016/j.jher.2011.07.002.
  45. Kharazi, B.A. and Behzadan, A.H. (2021), "Flood depth mapping in street photos with image processing and deep neural networks", Comput. Environ. Urban Syst., 88, 101628. https://doi.org/10.1016/j.compenvurbsys.2021.101628.
  46. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012), "Imagenet classification with deep convolutional neural networks", Adv. Neural Informat. Pr. Syst., 25, 1097-1105. 
  47. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017), "ImageNet classification with deep convolutional neural networks", Commun. ACM, 60(6), 84-90. https://doi.org/10.1145/3065386.
  48. Le Coz, J., Renard, B., Vansuyt, V., Jodeau, M. and Hauet, A. (2021), "Estimating the uncertainty of video-based flow velocity and discharge measurements due to the conversion of field to image coordinates", Hydrol. Process., 35(5), e14169. https://doi.org/10.1002/hyp.14169.
  49. Leitao, J.P., Pena-Haro, S., Luthi, B., Scheidegger, A. and de Vitry, M.M. (2018), "Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry", J. Hydrol., 565, 791-804. https://doi.org/10.1016/j.jhydrol.2018.09.001.
  50. Li, D.X., Zhong, Q., Yu, M.Z. and Wang, X.K. (2013), "Large-scale particle tracking velocimetry with multi-channel CCD cameras", Int. J. Sediment Res., 28(1), 103-110. https://doi.org/10.1016/S1001-6279(13)60022-0.
  51. Li, W., Liao, Q. and Ran, Q. (2019), "Stereo-imaging LSPIV (SI-LSPIV) for 3D water surface reconstruction and discharge measurement in mountain river flows", J. Hydrol., 578, 124099. https://doi.org/10.1016/j.jhydrol.2019.124099.
  52. Li, J., Kong, X., Yang, Y., Yang, Z. and Hu, J. (2022), "Optical flow based measurement of flow field in wave-structure interaction", Ocean Eng., 263, 112336. https://doi.org/10.1016/j.oceaneng.2022.112336.
  53. Li, J., Cai, R., Tan, Y., Zhou, H., Sadick, A.M., Shou, W. and Wang, X. (2023), "Automatic detection of actual water depth of urban floods from social media images", Measure., 216, 112891. https://doi.org/10.1016/j.measurement.2023.112891.
  54. Liang, Y., Jafari, N., Luo, X., Chen, Q., Cao, Y. and Li, X. (2020), "WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance", Comput. Visual Media, 6, 65-78. https://doi.org/10.1007/s41095-020-0156-x.
  55. Liang, Y., Li, X., Tsai, B., Chen, Q. and Jafari, N. (2023), "V-FloodNet: A video segmentation system for urban flood detection and quantification", Environ. Model. Softw., 160, 105586. https://doi.org/10.1016/j.envsoft.2022.105586.
  56. Lin, Y.T., Lin, Y.C. and Han, J.Y. (2018), "Automatic water-level detection using single-camera images with varied poses", Measure., 127, 167-174. https://doi.org/10.1016/j.measurement.2018.05.100.
  57. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y. and Berg, A.C. (2016), "Ssd: Single shot multibox detector", Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, Netherlands, October.
  58. Liu, W.C. and Huang, W.C. (2021), "Development of a three-axis accelerometer and large-scale particle image velocimetry (LSPIV) to enhance surface velocity measurements in rivers", Comput. Geosci., 155, 104866. https://doi.org/10.1016/j.cageo.2021.104866.
  59. Liu, W.C., Huang, W.C. and Young, C.C. (2022), "Uncertainty analysis for image-based streamflow measurement: The influence of ground control points", Water, 15(1), 123. https://doi.org/10.3390/w15010123.
  60. Liu, Y., Cheng, M. M., Hu, X., Wang, K., and Bai, X. (2017), "Richer convolutional features for edge detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., Honolulu, HI, USA, July.
  61. Lo, S.W., Wu, J.H., Lin, F.P. and Hsu, C.H. (2015), "Cyber surveillance for flood disasters", Sensors, 15(2), 2369-2387. https://doi.org/10.3390/s150202369.
  62. Lo, S.W., Wu, J.H., Chang, J.Y., Tseng, C.H., Lin, M.W. and Lin, F.P. (2021), "Deep sensing of urban waterlogging", IEEE Access, 9, 127185-127203. https://doi.org/10.1109/ACCESS.2021.3111623.
  63. Long, J., Shelhamer, E. and Darrell, T. (2015), "Fully convolutional networks for semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., Boston, MA, USA, June.
  64. Lucas, B.D. and Kanade, T. (1981), "An iterative image registration technique with an application to stereo vision", IJCAI'81: 7th International Joint Conference on Artificial Intelligence, Vancouver, Canada, August.
  65. Moy de Vitry, M., Kramer, S., Wegner, J.D. and Leitao, J.P. (2019), "Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network", Hydrol. Earth System Sci., 23(11), 4621-4634. https://doi.org/10.5194/hess-23-4621-2019.
  66. Muhadi, N.A., Abdullah, A.F., Bejo, S.K., Mahadi, M.R. and Mijic, A. (2020), "Image segmentation methods for flood monitoring system", Water, 12(6), 1825. https://doi.org/10.3390/w12061825.
  67. Munawar, H.S., Ullah, F., Qayyum, S., Khan, S.I. and Mojtahedi, M. (2021), "UAVs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection", Sustainab., 13(14), 7547. https://doi.org/10.3390/su13147547.
  68. Muste, M., Hauet, A., Fujita, I., Legout, C. and Ho, H.C. (2014), "Capabilities of large-scale particle image velocimetry to characterize shallow free-surface flows", Adv. Water Res., 70, 160-171. https://doi.org/10.1016/j.advwatres.2014.04.004.
  69. Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M. and Suarez, J. (2019), "Using a 2D shallow water model to assess large-scale particle image velocimetry (LSPIV) and structure from motion (SfM) techniques in a street-scale urban drainage physical model", J. Hydrol., 575, 54-65. https://doi.org/10.1016/j.jhydrol.2019.05.003.
  70. Naves, J., Garcia, J.T., Puertas, J. and Anta, J. (2021), "Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model", Hydrol. Earth Syst. Sci., 25(2), 885-900. https://doi.org/10.5194/hess-25-885-2021.
  71. Ning, H., Li, Z., Hodgson, M.E. and Wang, C. (2020), "Prototyping a social media flooding photo screening system based on deep learning", ISPRS Int. J. Geo-Informat., 9(2), 104. https://doi.org/10.3390/ijgi9020104.
  72. Pally, R.J. and Samadi, S. (2022), "Application of image processing and convolutional neural networks for flood image classification and semantic segmentation", Environ. Model. Softw., 148, 105285. https://doi.org/10.1016/j.envsoft.2021.105285.
  73. Pan, J., Yin, Y., Xiong, J., Luo, W., Gui, G. and Sari, H. (2018), "Deep learning-based unmanned surveillance systems for observing water levels", IEEE Access, 6, 73561-73571. https://doi.org/10.1109/ACCESS.2018.2883702.
  74. Park, S., Baek, F., Sohn, J. and Kim, H. (2021), "Computer vision-based estimation of flood depth in flooded-vehicle images", J. Comput. Civil Eng., 35(2), 04020072. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000956.
  75. Pereira, J., Monteiro, J., Silva, J., Estima, J. and Martins, B. (2020), "Assessing flood severity from crowdsourced social media photos with deep neural networks", Multimed. Tools Appl., 79, 26197-26223. https://doi.org/10.1007/s11042-020-09196-8.
  76. Perks, M.T., Russell, A.J. and Large, A.R. (2016), "Advances in flash flood monitoring using unmanned aerial vehicles (UAVs)", Hydrol. Earth System Sci., 20(10), 4005-4015. https://doi.org/10.5194/hess-20-4005-2016.
  77. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016), "You only look once: Unified, real-time object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., Las Vegas, NV, USA, June.
  78. Redmon, J. and Farhadi, A. (2018), "YOLOv3: An incremental improvement", arXiv preprint arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767.
  79. Ren, S., He, K., Girshick, R. and Sun, J. (2016), "Faster R-CNN: Towards real-time object detection with region proposal networks", IEEE Transac. Pattern Anal. Mach. Intell., 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031.
  80. Ronneberger, O., Fischer, P. and Brox, T. (2015), "U-Net: Convolutional networks for biomedical image segmentation", Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference., Munich, Germany, October.
  81. Sabbatini, L., Palma, L., Belli, A., Sini, F. and Pierleoni, P. (2021), "A computer vision system for staff gauge in river flood monitoring", Invent., 6(4), 79. https://doi.org/10.3390/inventions6040079.
  82. Seibert, J., Strobl, B., Etter, S., Hummer, P. and van Meerveld, H.J. (2019), "Virtual staff gauges for crowd-based stream level observations", Front. Earth Sci., 7, 70. https://doi.org/10.3389/feart.2019.00070.
  83. Simonyan, K. and Zisserman, A. (2014), "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556.
  84. Smith, M.W., Carrivick, J.L., Hooke, J. and Kirkby, M.J. (2014), "Reconstructing flash flood magnitudes using 'Structure-from-motion': A rapid assessment tool", J. Hydrol., 519, 1914-1927. https://doi.org/10.1016/j.jhydrol.2014.09.078.
  85. Sobel, I. and Feldman, G. (1968), "An isotropic 3×3 gradient operator for image processing", Mach. Vis. Three-Dimens. Scenes, 1968, 376-379.
  86. Strobl, B., Etter, S., van Meerveld, I. and Seibert, J. (2020), "Accuracy of crowdsourced streamflow and stream level class estimates", Hydrol. Sci. J., 65(5), 823-841. https://doi.org/10.1080/02626667.2019.1578966.
  87. Tauro, F., Piscopia, R. and Grimaldi, S. (2017), "Streamflow observations from cameras: Large-scale particle image velocimetry or particle tracking velocimetry?", Water Resour. Res., 53(12), 10374-10394. https://doi.org/10.1002/2017WR020848.
  88. Tauro, F., Tosi, F., Mattoccia, S., Toth, E., Piscopia, R. and Grimaldi, S. (2018), "Optical tracking velocimetry (OTV): Leveraging optical flow and trajectory-based filtering for surface streamflow observations", Remote Sens., 10(12), 2010. https://doi.org/10.3390/rs10122010.
  89. Teed, Z. and Deng, J. (2020), "Raft: Recurrent all-pairs field transforms for optical flow", Computer Vision-ECCV 2020: 16th European Conference., Glasgow, Scotland, August.
  90. Thuerey, N. and Pfaff, T. (2018), MantaFlow, http://mantaflow.com
  91. Tosi, F., Rocca, M., Aleotti, F., Poggi, M., Mattoccia, S., Tauro, F., Toth, E. and Grimaldi, S. (2020), "Enabling image-based streamflow monitoring at the edge", Remote Sens., 12(12), 2047. https://doi.org/10.3390/rs12122047.
  92. Tsubaki, R., Fujita, I. and Tsutsumi, S. (2011), "Measurement of the flood discharge of a small-sized river using an existing digital video recording system", J. Hydro-environ. Res., 5(4), 313-321. https://doi.org/10.1016/j.jher.2010.12.004.
  93. Vandaele, R., Dance, S.L. and Ojha, V. (2021), "Deep learning for automated river-level monitoring through river-camera images: An approach based on water segmentation and transfer learning", Hydrol. Earth System Sci., 25(8), 4435-4453. https://doi.org/10.5194/hess-25-4435-2021.
  94. Vanden Boomen, R.L., Yu, Z. and Liao, Q. (2021), "Application of deep learning for imaging-based stream gaging", Water Resour. Res., 57(11), e2021WR029980. https://doi.org/10.1029/2021WR029980.
  95. Vezhnevets, V. and Konouchine, V. (2005). "GrowCut: Interactive multi-label ND image segmentation by cellular automata", Proc. Graphicon, 1(4), 150-156.
  96. Wagner, F., Eltner, A. and Maas, H.G. (2023), "River water segmentation in surveillance camera images: A comparative study of offline and online augmentation using 32 CNNs", Int. J. Appl. Earth Observ. Geoinformat., 119, 103305. https://doi.org/10.1016/j.jag.2023.103305.
  97. Wang, R.Q., Mao, H., Wang, Y., Rae, C. and Shaw, W. (2018), "Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data", Comput. Geosci., 111, 139-147. https://doi.org/10.1016/j.cageo.2017.11.008.
  98. Wang, R.Q. and Ding, Y. (2022), "Semi-supervised identification and mapping of surface water extent using street-level monitoring videos", Big Earth Data, 7(4), 986-1004. https://doi.org/10.1080/20964471.2022.2123352.
  99. Wang, Z., Seibert, J., van Meerveld, I., Lyu, H. and Zhang, C. (2023), "Automatic water-level class estimation from repeated crowd-based photos of streams", Hydrol. Sci. J., 68(13), 1826-1840. https://doi.org/10.1080/02626667.2023.2240312.
  100. Wu, H., Zhao, R., Gan, X. and Ma, X. (2019), "Measuring surface velocity of water flow by dense optical flow method", Water, 11(11), 2320. https://doi.org/10.3390/w11112320.
  101. Wu, Y., Zhang, J., Cao, Y., Wang, Z., Zhang, G. and Hou, D. (2023), "River surface velocimetry based on virtual river dataset and modulated-RAFT", IEEE Access., 11, 38275-38290. https://doi.org/10.1109/ACCESS.2023.3267635.
  102. Xie, S. and Tu, Z. (2015), "Holistically-nested edge detection", Proceedings of the IEEE International Conference on Computer Vision., Santiago, Chile, December.
  103. Yeh, M.T., Chung, Y.N., Huang, Y.X., Lai, C.W. and Juang, D.J. (2019), "Applying adaptive LS-PIV with dynamically adjusting detection region approach on the surface velocity measurement of river flow," Comput. Electr. Eng., 74, 466-482. https://doi.org/10.1016/j.compeleceng.2017.12.013.
  104. Zhang, Z., Zhou, Y., Liu, H., Zhang, L. and Wang, H. (2019), "Visual measurement of water level under complex illumination conditions", Sensors, 19(19), 4141. https://doi.org/10.3390/s19194141.
  105. Zhang, D. and Tong, J. (2023), "Robust water level measurement method based on computer vision", J. Hydrol., 620, 129456. https://doi.org/10.1016/j.jhydrol.2023.129456.
  106. Zhu, X. and Lipeme Kouyi, G. (2019), "An analysis of LSPIV-based surface velocity measurement techniques for stormwater detention basin management", Water Resour. Res., 55(2), 888-903. https://doi.org/10.1029/2018WR023813.
  107. Zou, Z., Chen, K., Shi, Z., Guo, Y. and Ye, J. (2023), "Object detection in 20 years: A survey", Proc. IEEE., 111(3), 257-276. https://doi.org/10.1109/JPROC.2023.3238524.