Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4621-2019
© Author(s) 2019. 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-23-4621-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2019
Research article |
| 15 Nov 2019
| 15 Nov 2019
Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network
Matthew Moy de Vitry
CORRESPONDING AUTHOR
Department of Urban Water Management, Eawag – Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Institute of Environmental Engineering, ETH Zurich, 8093 Zürich, Switzerland
Simon Kramer
Institute of Environmental Engineering, ETH Zurich, 8093 Zürich, Switzerland
Jan Dirk Wegner
EcoVision Lab, Photogrammetry and Remote Sensing group, ETH Zurich,
8093 Zürich, Switzerland
João P. Leitão
Department of Urban Water Management, Eawag – Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
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- HydrocamCollect: A robust data acquisition and cloud data transfer workflow for camera-based hydrological monitoring S. Neupane et al. https://doi.org/10.1016/j.envsoft.2025.106770
- Quantifying flood model accuracy under varying surface complexities W. Addison-Atkinson et al. https://doi.org/10.1016/j.jhydrol.2023.129511
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- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. https://doi.org/10.1017/eds.2023.6
- Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. https://doi.org/10.1016/j.jhydrol.2022.127726
- Refraction-based waterlogging depth measurement using solely traffic cameras for transparent flood monitoring J. Qin & P. Shen https://doi.org/10.1016/j.jhydrol.2025.132917
- Robust water level measurement method based on computer vision D. Zhang & J. Tong https://doi.org/10.1016/j.jhydrol.2023.129456
- A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar H. Shui et al. https://doi.org/10.3390/s22031236
- A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions Q. Zhou et al. https://doi.org/10.5194/hess-27-1791-2023
- Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. https://doi.org/10.5194/hess-28-5443-2024
- An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system W. Zhu et al. https://doi.org/10.5194/hess-27-2035-2023
- Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management R. Vandaele et al. https://doi.org/10.2166/hydro.2024.013
- Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan https://doi.org/10.1007/s43762-023-00090-1
- floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time J. Hofmann & H. Schüttrumpf https://doi.org/10.3390/w13162255
- Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices Y. Lee et al. https://doi.org/10.3390/rs16132358
- The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão https://doi.org/10.1016/j.watres.2020.115669
- The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index L. Scheiber et al. https://doi.org/10.5194/nhess-23-2313-2023
- Identification of pedestrian submerged parts in urban flooding based on images and deep learning J. Jiang et al. https://doi.org/10.1016/j.envsoft.2024.106252
- A deep learning model for predicting river flood depth and extent H. Hosseiny https://doi.org/10.1016/j.envsoft.2021.105186
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Saved (final revised paper)
Latest update: 22 Oct 2026
Short summary
This work demonstrates a new approach to obtain flood level trend information from surveillance footage with minimal prior information. A neural network trained to detect flood water is applied to video frames to create a qualitative flooding metric (namely, SOFI). The correlation between the real water trend and SOFI was found to be 75 % on average (based on six videos of flooding under various circumstances). SOFI could be used for flood model calibration, to increase model reliability.
This work demonstrates a new approach to obtain flood level trend information from surveillance...
