Articles | Volume 29, issue 14
https://doi.org/10.5194/hess-29-3165-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3165-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras
SABO Department, National Institute for Land and Infrastructure Management, Tsukuba, Japan
Taro Uchida
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Related authors
No articles found.
Thiago D. dos Santos and Taro Uchida
Nat. Hazards Earth Syst. Sci., 25, 4153–4180, https://doi.org/10.5194/nhess-25-4153-2025, https://doi.org/10.5194/nhess-25-4153-2025, 2025
Short summary
Short summary
Five federal sediment-related disaster risk assessments have been conducted in Brazil, each with distinct objectives and methodologies. To evaluate their effectiveness and identify issues, we analyzed the methods and outcome data and reviewed the status of disaster prevention initiatives based on the assessment results. Our findings revealed persistent problems across all methods. Consequently, we recommend improvements to enhance the methods' efficacy and reliability.
Cited articles
Alcantarilla, P. F., Nuevo, J., and Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces, Proceedings of 24th British Machine Vision Conference (BMVC), Bristol, UK, 9–13 September 2013, 13.1–13.11, https://doi.org/10.5244/C.27.13, 2013.
Allamano, P., Croci, A., and Laio, F.: Toward the camera rain gauge, Water Resour. Res., 51, 1744–1757, https://doi.org/10.1002/2014WR016298, 2015.
Bossu, J., Hautière, N., and Tarel, J. P.: Rain or snow detection in image sequences through use of a histogram of orientation of streaks, Int. J. Comput. Vision, 93, 348–367, https://doi.org/10.1007/s11263-011-0421-7, 2011.
Bradley, S. G., Stow, C. D., and Lynch-Blosse, C. A.: Measurements of rainfall properties using long optical path imaging, J. Atmos. Ocean. Tech., 17, 761–772, https://doi.org/10.1175/1520-0426(2000)017<0761:MORPUL>2.0.CO;2, 2000.
Chylek, P.: A note on extinction and scattering efficiencies, J. Appl. Meteorol., 16, 321–322, 1977.
Dong, R., Liao, J., Li, B., Zhou, H., and Crookes, D.: Measurements of rainfall rates from videos, Proceedings of 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 14–16 October 2017, 1–9, https://doi.org/10.1109/CISP-BMEI.2017.8302066, 2017.
Eltahir, E. A. B. and Bras, R. L.: Precipitation recycling, Rev. Geophys., 34, 367–378, https://doi.org/10.1029/96RG01927, 1996.
Fattal, R.: Single image dehazing, ACM T. Graphic., 27, https://doi.org/10.1145/1360612.1360671, 2008.
Garg, K. and Nayar, S. K.: Vision and rain, Int. J. Comput. Vision, 75, 3–27, https://doi.org/10.1007/s11263-006-0028-6, 2007.
Geospatial Information Authority of Japan: The digital elevation models, Geospatial Information Authority of Japan, https://www.gsi.go.jp/kiban/index.html, 19 February 2018.
Gilmore, T. E., Birgand, F., and Chapman, K. W.: 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, 2013.
Grabner, M. and Kvicera, V.: The wavelength dependent model of extinction in fog and haze for free space optical communication, Opt. Express, 19, 3379–3386, https://doi.org/10.1364/oe.19.003379, 2011.
Haberlandt, U. and Sester, M.: Areal rainfall estimation using moving cars as rain gauges – a modelling study, Hydrol. Earth Syst. Sci., 14, 1139–1151, https://doi.org/10.5194/hess-14-1139-2010, 2010.
He, K., Sun, J., and Tang, X.: Single image haze removal using dark channel prior, IEEE T. Pattern Anal., 33, 2341–2353, https://doi.org/10.1109/TPAMI.2010.168, 2011.
Jacobs, N., Burgin, W., Fridrich, N., Abrams, A., Miskell, K., Braswell, B. H., Richardson, A. D., and Pless, R.: The global network of outdoor webcams: Properties and applications. ACM International Symposium on Advances in Geographic Information Systems, 111–120, https://doi.org/10.1145/1653771.1653789, 2009.
Jiang, S., Babovic, V., Zheng, Y., and Xiong, J.: Advancing opportunistic sensing in Hydrology: A novel approach to measuring rainfall with ordinary surveillance cameras, Water Resour. Res., 55, 3004–3027, https://doi.org/10.1029/2018WR024480, 2019.
Kanazawa, A.: Dataset for “Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras” (1/3), Zenodo [data set], https://doi.org/10.5281/zenodo.7163149, 2022a.
Kanazawa, A.: Dataset for “Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras” (2/3), Zenodo [data set], https://doi.org/10.5281/zenodo.7166150, 2022b.
Kanazawa, A.: Dataset for “Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras” (3/3), Zenodo [data set], https://doi.org/10.5281/zenodo.7166178, 2022c.
Kanazawa, A.: Supplement for “Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras”, Zenodo [data set], https://doi.org/10.5281/zenodo.13337020, 2024.
Kanazawa, A.: Codes for “Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras”, Zenodo [code], https://doi.org/10.5281/zenodo.16305949, 2025.
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., and Kirschbaum, D. B.: So, how much of the Earth's surface is covered by rain gauges?, B. Am. Meteorol. Soc., 98, 69–78, https://doi.org/10.1175/BAMS-D-14-00283.1, 2017.
Kim, D. and Noh, Y.: An aerosol extinction coefficient retrieval method and characteristics analysis of landscape images, Sensors, 21, 7282, https://doi.org/10.3390/s21217282, 2021.
Koschmieder, H.: Theorie der horizontalen sichtweite, Beitrage zur Physik der freien Atmosphare, 12, 171–181, 1924.
Lee, J., Byun, J., Baik, J., Jun, C., and Kim, H.-J.: Estimation of raindrop size distribution and rain rate with infrared surveillance camera in dark conditions, Atmos. Meas. Tech., 16, 707–725, https://doi.org/10.5194/amt-16-707-2023, 2023.
Leijnse, H., Uijlenhoet, R., and Stricker, J. N. M.: Rainfall measurement using radio links from cellular communication networks, Water Resour. Res., 43, W03201, https://doi.org/10.1029/2006WR005631, 2007.
Li, R., Tan, R. T., and Cheong. L.-F.: Robust optical flow in rainy scenes, Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September, 299–317, https://doi.org/10.1007/978-3-030-01267-0_18, 2018.
Li, S., Araujo, I. B., Ren, W., Wang, Z., Tokuda, E. K., Hirata Jr. R., Cesar Jr., R., Zhang, J., Guo, X., and Cao, X.: Single image deraining: A comprehensive benchmark analysis, Proc. CVPR IEEE, Long Beach, CA, USA, 15–20 June 2019, 3833–3842, https://doi.org/10.1109/CVPR.2019.00396, 2019.
Lin, C. W., Lin, M. X., and Yang, S. H.: SOPNet Method for the Fine-Grained Measurement and Prediction of Precipitation Intensity Using Outdoor Surveillance Cameras, IEEE Access, 8, 188813–188824, https://doi.org/10.1109/Access.2020.3032430, 2020.
Lin, C. W., Huang, X., Lin, M., and Hong, S.: SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation, Sensors, 22, 511, https://doi.org/10.3390/s22020551, 2022.
Luo, Y., Xu, Y., and Ji, H.: Removing rain from a single image via discriminative sparse coding, Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015, 3397–3405, https://doi.org/10.1109/ICCV.2015.388, 2015.
Marshall, J. S. and Palmer, W. M. K.: The distribution of raindrops with size, J. Meteorol., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948.
Marshall, J. S., Hitschfeld, W., and Gunn, K. L. S.: Advances in radar weather, Adv. Geophys., 2, 1–56, https://doi.org/10.1016/S0065-2687(08)60310-6, 1955.
Meng, G., Wang, Y., Duan, J., Xiang, S., and Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization, Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, NSW, Australia, 1–8 December 2013, 617–624, https://doi.org/10.1109/ICCV.2013.82, 2013.
Messer, H., Zinevich, A., and Alpert, P.: Environmental monitoring by wireless communication networks, Science, 312, 713, https://doi.org/10.1126/science.1120034, 2006.
Muste, M., Fujita, I., and Hauet, A.: Large-scale particle image velocimetry for measurements in riverine environments, Water Resour. Res., 46, W00D19, https://doi.org/10.1029/2008WR006950, 2008.
Narasimhan, S. G. and Nayar, S. K.: Vision and the atmosphere, Int. J. Comput. Vision, 48, 233–254, https://doi.org/10.1023/A:1016328200723, 2002.
Narasimhan, S. G. and Nayar, S. K.: Contrast restoration of weather degraded images, IEEE T. Pattern Anal., 25. 713–724, https://doi.org/10.1109/TPAMI.2003.1201821, 2003.
Natural Earth: Coastline, Natural Earth, https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/, last access: 13 August 2022.
Nedvidek, F., Schneider, C., Kucerovsky, Z., and Brannen, E.: Near-infrared extinction in rain measured using a single detector system, J. Atmos. Ocean. Tech., 3, 391–399, https://doi.org/10.1175/1520-0426(1986)003<0391:NIEIRM>2.0.CO;2, 1986.
Notarangelo, N. M., Hirano, K., Albano, R., and Sole, A.: Transfer learning with convolutional neural networks for rainfall detection in single images, Water, 13, 588, https://doi.org/10.3390/w13050588, 2021.
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Measuring urban rainfall using microwave links from commercial cellular communication networks, Water Resour. Res., 47, W12505, https://doi.org/10.1029/2010WR010350, 2011.
Ozkaynak, H., Schatz, A. D., Thurston, G. D., Isaacs, R. G., and Husar, R. B.: Relationships between aerosol extinction coefficients derived from airport visual range observations and alternative measures of airborne particle mass, JAPCA J. Air Waste Ma., 35, 1176–1185, https://doi.org/10.1080/00022470.1985.10466020, 1985.
Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H.: FFA-Net: Feature Fusion Attention Network for Single Image Dehazing, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11908–11915, https://doi.org/10.1609/aaai.v34i07.6865, 2020.
Qu, Y., Chen, Y., Huang, J., and Xie, Y.: Enhanced PIX2PIX dehazing network, Proc. CVPR IEEE, Long Beach, CA, USA, 15–20 June 2019, 8152–8160, https://doi.org/10.1109/CVPR.2019.00835, 2019.
Rabiei, E., Haberlandt, U., Sester, M., Fitzner, D., and Wallner, M.: Areal rainfall estimation using moving cars – computer experiments including hydrological modeling, Hydrol. Earth Syst. Sci., 20, 3907–3922, https://doi.org/10.5194/hess-20-3907-2016, 2016.
Rahimi, A. R., Holt, A. R., Upton, G. J. G., Krämer, S., Redder, A., and Verworn, H. R.: Attenuation calibration of an X-band weather radar using a microwave link, J. Atmos. Ocean. Tech., 23, 395–405, https://doi.org/10.1175/JTECH1855.1, 2006.
Ren, D., Zuo, W., Hu, Q., Zhu, P., and Meng, D.: Progressive image deraining networks: A better and simpler baseline, Proc. CVPR IEEE, Long Beach, CA, USA, 15–20 June 2019, 3932–3941, https://doi.org/10.1109/CVPR.2019.00406, 2019.
Rogers, R. R., Lamoureux, M. F., Bissonnette, L. R., and Peters, R. M.: Quantitative interpretation of laser ceilometer intensity profiles, J. Atmos. Ocean. Tech., 14, 396–411, https://doi.org/10.1175/1520-0426(1997)014<0396:QIOLCI>2.0.CO;2, 1997.
Serio, M. A., Carollo, F. G., and Ferro, V.: Raindrop size distribution and terminal velocity for rainfall erosivity studies. A review, J. Hydrol., 576, 210–228, https://doi.org/10.1016/j.jhydrol.2019.06.040, 2019.
Shao, Y., Li, L., Ren, W., Gao, C., and Sang, N.: Domain adaptation for image dehazing, Proc. CVPR IEEE, Seattle, WA, USA, 13–19 June 2020, 2805–2814, https://doi.org/10.1109/CVPR42600.2020.00288, 2020.
Shin, J., Kim, D., and Noh, Y.: Estimation of Aerosol Extinction Coefficient Using Camera Images and Application in Mass Extinction Efficiency Retrieval, Remote Sens., 14, 1224, https://doi.org/10.3390/rs14051224, 2022.
Shipley, S. T., Eloranta, E. W., and Weinman, J. A.: Measurement of rainfall rates by lidar, J. Appl. Meteorol., 13, 800–807, https://doi.org/10.1175/1520-0450(1974)013<0800:MORRBL>2.0.CO;2, 1974.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L.: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018.
Suriza, A. Z., Md Rafiqul, I., Wajdi, A. K., and Naji, A. W.: Proposed parameters of specific rain attenuation prediction for Free Space Optics link operating in tropical region, J. Atmos. Sol.-Terr. Phy., 94, 93–99, https://doi.org/10.1016/j.jastp.2012.11.008, 2013.
Tan, R. T.: Visibility in bad weather from a single image. Proc. CVPR IEEE, Anchorage, AK, USA, 23–28 June 2008, https://doi.org/10.1109/CVPR.2008.4587643, 2008.
Tauro, F., Selker, J., Van De Giesen, N., Abrate, T., Uijlenhoet, R., Porfiri, M., Manfreda, S., Caylor, K., Moramarco, T., Benveniste, J., Ciraolo, G., Estes, L., Domeneghetti, A., Perks, M. T., Corbari, C., Rabiei, E., Ravazzani, G., Bogena, H., Harfouche, A., Brocca, L., Maltese, A., Wickert, A., Tarpanelli, A., Good, S., Alcala, J. M. L., Petroselli, A., Cudennec, C., Blume, T., Hut, R., and Grimaldi, S.: Measurements and observations in the XXI century (MOXXI): Innovation and multi-disciplinarity to sense the hydrological cycle, Hydrolog. Sci. J., 63, 169–196, https://doi.org/10.1080/02626667.2017.1420191, 2018.
Tripathi, A. K. and Mukhopadhyay, S.: Efficient fog removal from video, Signal Image Video P., 8, 1431–1439, https://doi.org/10.1007/s11760-012-0377-2, 2014.
Uchiyama, A., Yamazaki, A., Kudo, R., Kobayashi, E., Togawa, H., and Uesawa, D.: Continuous ground-based observation of aerosol optical properties at Tsukuba, Japan: Trend and climatology, J. Meteorol. Soc. Jpn., 92A, 93–108, https://doi.org/10.2151/jmsj.2014-A06, 2014.
Uchiyama, A., Chen, B., Yamazaki, A., Shi, G., Kudo, R., Nishita-Hara, C., Hayashi, M., Habib, A., and Matsunaga, T.: Aerosol optical characteristics in Fukuoka and Beijing measured by integrating nephelometer and aethalometer: Comparison of source and downstream regions, J. Meteorol. Soc. Jpn., 96, 215–240, https://doi.org/10.2151/jmsj.2018-026, 2018.
Uijlenhoet, R.: Raindrop size distributions and radar reflectivity–rain rate relationships for radar hydrology, Hydrol. Earth Syst. Sci., 5, 615–628, https://doi.org/10.5194/hess-5-615-2001, 2001.
Uijlenhoet, R., Cohard, J. M., and Gosset, M.: Path-average rainfall estimation from optical extinction measurements using a large-aperture scintillometer, J. Hydrometeorol., 12, 955–972, https://doi.org/10.1175/2011JHM1350.1, 2011.
Uijlenhoet, R., Overeem, A., and Leijnse, H.: Opportunistic remote sensing of rainfall using microwave links from cellular communication networks, WIREs Water, 2018; 5:e1289, https://doi.org/10.1002/wat2.1289, 2018.
Ulbrich, C. W. and Atlas, D.: Extinction of visible and infrared radiation in rain: Comparison of theory and experiment, J. Atmos. Ocean. Tech., 2, 331–339, https://doi.org/10.1175/1520-0426(1985)002<0331:EOVAIR>2.0.CO;2, 1985.
Upton, G. J. G., Holt, A. R., Cummings, R. J., Rahimi, A. R., and Goddard, J. W. F.: Microwave links: The future for urban rainfall measurement?, Atmos. Res., 77, 300–312, https://doi.org/10.1016/j.atmosres.2004.10.009, 2005.
Wang, H., Yue, Z. S., Xie, Q., Zhao, Q., Zheng, Y., and Meng, D.: From Rain Generation to Rain Removal, Proc. CVPR IEEE, 20–25 June 2021, 14786–14796, https://doi.org/10.1109/CVPR46437.2021.01455, 2021.
Wang, X., Wang, M., Liu, X., Zhu, L., Shi, S., Glade, T., Chen, M., Xie, Y., Wu, Y., and He, Y.: Near-infrared surveillance video-based rain gauge, J. Hydrol., 618, 129173, https://doi.org/10.1016/j.jhydrol.2023.129173, 2023.
World Meteorological Organization: Guide to Instruments and Methods of Observation (WMO-No. 8), in: Measurement of Meteorological Variables, vol. I, World Meteorological Organization, 574 pp., https://library.wmo.int/idurl/4/41650, 2023.
Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., Xie, Y., and Ma, L.: Contrastive Learning for Compact Single Image Dehazing, Proc. CVPR IEEE, Nashville, TN, USA, 20–25 June 2021, 10551–10560, https://doi.org/10.1109/CVPR46437.2021.01041, 2021.
Yan, K., Chen, H., Hu, L., Huang, K., Huang, Y., Wang, Z., Liu, B., Wang, J., and Guo, S.: A review of video-based rainfall measurement methods, WIREs Water, 2023;10:e1678, https://doi.org/10.1002/wat2.1678, 2023.
Yang, D. and Sun, J.: Proximal dehaze-net: A prior learning-based deep network for single image dehazing, Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018, 729–746, https://doi.org/10.1007/978-3-030-01234-2_43, 2018.
Yin, H., Zheng, F., Duan, H.-F., Savic, D., and Kaplean, Z.: Estimating Rainfall Intensity Using an Image-Based Deep Learning Model, Engineering, 21, 162–174, https://doi.org/10.1016/j.eng.2021.11.021, 2023.
Zaki, R. W., Fayed, H. A., El Aziz, A. A., and Aly, M. H.: Outdoor visible light communication in intelligent transportation systems: Impact of snow and rain, Appl. Sci., 9, 5453, https://doi.org/10.3390/app9245453, 2019.
Zhang, H.: image_dehaze, https://github.com/He-Zhang/image_dehaze (last access: 5 October 2022), 2021.
Zheng, F., Yin, H., Ma, Y., Duan, H.-F., Gupta, H., Savic, D., and Kapelan, Z.: Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras, Water Resour. Res., 59, e2023WR034831, https://doi.org/10.1029/2023WR034831, 2023.
Zhou, C., Teng, M., Han, Y., Xu, C., and Shi, B.: Learning to dehaze with polarization, Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), virtual conference, 6–14 December 2021, 11487–11500, 2021.
Zinevich, A., Messer, H., and Alpert, P.: Frontal rainfall observation by a commercial microwave communication network, J. Appl. Meteorol. Clim., 48, 1317–1334, https://doi.org/10.1175/2008JAMC2014.1, 2009.
Short summary
Camera-based rainfall observation can measure rainfall with high spatiotemporal resolution and low cost. This study proposed a method for estimating rainfall intensity from images using the relationship between image information and rainfall intensity. The method was applied to outdoor cameras, and rainfall intensity could be estimated from the images. The method has the potential to facilitate the development of a camera-based rainfall observation technology that is accurate and versatile.
Camera-based rainfall observation can measure rainfall with high spatiotemporal resolution and...