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
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
Viewed
Total article views: 4,354 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Feb 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,592 | 1,720 | 42 | 4,354 | 168 | 64 | 47 |
- HTML: 2,592
- PDF: 1,720
- XML: 42
- Total: 4,354
- Supplement: 168
- BibTeX: 64
- EndNote: 47
Total article views: 2,856 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Nov 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,976 | 842 | 38 | 2,856 | 168 | 52 | 37 |
- HTML: 1,976
- PDF: 842
- XML: 38
- Total: 2,856
- Supplement: 168
- BibTeX: 52
- EndNote: 37
Total article views: 1,498 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Feb 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 616 | 878 | 4 | 1,498 | 12 | 10 |
- HTML: 616
- PDF: 878
- XML: 4
- Total: 1,498
- BibTeX: 12
- EndNote: 10
Viewed (geographical distribution)
Total article views: 4,354 (including HTML, PDF, and XML)
Thereof 3,742 with geography defined
and 612 with unknown origin.
Total article views: 2,856 (including HTML, PDF, and XML)
Thereof 2,519 with geography defined
and 337 with unknown origin.
Total article views: 1,498 (including HTML, PDF, and XML)
Thereof 1,223 with geography defined
and 275 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
44 citations as recorded by crossref.
- Robust water level measurement method based on computer vision D. Zhang & J. Tong 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. 10.3390/s22031236
- Field performance of the GaugeCam image-based water level measurement system F. Birgand et al. 10.1371/journal.pwat.0000032
- A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
- Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. 10.3390/ijerph19010237
- Real-time social media sentiment analysis for rapid impact assessment of floods L. Bryan-Smith et al. 10.1016/j.cageo.2023.105405
- ATeX: A Benchmark for Image Classification of Water in Different Waterbodies Using Deep Learning Approaches S. Erfani & E. Goharian 10.1061/(ASCE)WR.1943-5452.0001615
- The need for training and benchmark datasets for convolutional neural networks in flood applications A. Khouakhi et al. 10.2166/nh.2022.093
- Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
- The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
- Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network R. Dhaya & R. Kanthavel 10.32604/iasc.2022.021538
- A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions Q. Zhou et al. 10.5194/hess-27-1791-2023
- Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management M. Oberascher et al. 10.1016/j.scs.2021.103442
- 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. 10.5194/hess-27-2035-2023
- Microscopic identification of brazilian commercial wood species via machine-learning J. Moulin et al. 10.1590/01047760202228012978
- Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects D. Chitwatkulsiri & H. Miyamoto 10.3390/w15010178
- Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan 10.1007/s43762-023-00090-1
- Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms Z. Qiao et al. 10.3390/rs13224662
- Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee 10.3390/w14010099
- An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks L. Windheuser et al. 10.1029/2022EA002385
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
- floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time J. Hofmann & H. Schüttrumpf 10.3390/w13162255
- A two-stage image process for water level recognition via dual-attention CornerNet and CTransformer R. Qiu et al. 10.1007/s00371-022-02501-6
- A review of recent advances in urban flood research C. Agonafir et al. 10.1016/j.wasec.2023.100141
- Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas W. Mobley et al. 10.5194/nhess-21-807-2021
- Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
- The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão 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. 10.5194/nhess-23-2313-2023
- Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
- Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations A. Moreno-Rodenas et al. 10.1016/j.watres.2021.117482
- Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li 10.3233/JIFS-219033
- A deep learning model for predicting river flood depth and extent H. Hosseiny 10.1016/j.envsoft.2021.105186
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
- Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. 10.5194/nhess-23-1-2023
- Hydraulic modelling of inland urban flooding: Recent advances E. Mignot & B. Dewals 10.1016/j.jhydrol.2022.127763
- The role of deep learning in urban water management: A critical review G. Fu et al. 10.1016/j.watres.2022.118973
- Deep Sensing of Urban Waterlogging S. Lo et al. 10.1109/ACCESS.2021.3111623
- Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
- Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data J. Jiang et al. 10.3390/rs12061014
- Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
- Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim 10.3390/w13243545
- Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks Z. Guo et al. 10.1111/jfr3.12684
43 citations as recorded by crossref.
- Robust water level measurement method based on computer vision D. Zhang & J. Tong 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. 10.3390/s22031236
- Field performance of the GaugeCam image-based water level measurement system F. Birgand et al. 10.1371/journal.pwat.0000032
- A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
- Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. 10.3390/ijerph19010237
- Real-time social media sentiment analysis for rapid impact assessment of floods L. Bryan-Smith et al. 10.1016/j.cageo.2023.105405
- ATeX: A Benchmark for Image Classification of Water in Different Waterbodies Using Deep Learning Approaches S. Erfani & E. Goharian 10.1061/(ASCE)WR.1943-5452.0001615
- The need for training and benchmark datasets for convolutional neural networks in flood applications A. Khouakhi et al. 10.2166/nh.2022.093
- Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
- The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
- Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network R. Dhaya & R. Kanthavel 10.32604/iasc.2022.021538
- A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions Q. Zhou et al. 10.5194/hess-27-1791-2023
- Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management M. Oberascher et al. 10.1016/j.scs.2021.103442
- 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. 10.5194/hess-27-2035-2023
- Microscopic identification of brazilian commercial wood species via machine-learning J. Moulin et al. 10.1590/01047760202228012978
- Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects D. Chitwatkulsiri & H. Miyamoto 10.3390/w15010178
- Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan 10.1007/s43762-023-00090-1
- Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms Z. Qiao et al. 10.3390/rs13224662
- Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee 10.3390/w14010099
- An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks L. Windheuser et al. 10.1029/2022EA002385
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
- floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time J. Hofmann & H. Schüttrumpf 10.3390/w13162255
- A two-stage image process for water level recognition via dual-attention CornerNet and CTransformer R. Qiu et al. 10.1007/s00371-022-02501-6
- A review of recent advances in urban flood research C. Agonafir et al. 10.1016/j.wasec.2023.100141
- Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas W. Mobley et al. 10.5194/nhess-21-807-2021
- Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
- The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão 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. 10.5194/nhess-23-2313-2023
- Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
- Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations A. Moreno-Rodenas et al. 10.1016/j.watres.2021.117482
- Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li 10.3233/JIFS-219033
- A deep learning model for predicting river flood depth and extent H. Hosseiny 10.1016/j.envsoft.2021.105186
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
- Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. 10.5194/nhess-23-1-2023
- Hydraulic modelling of inland urban flooding: Recent advances E. Mignot & B. Dewals 10.1016/j.jhydrol.2022.127763
- The role of deep learning in urban water management: A critical review G. Fu et al. 10.1016/j.watres.2022.118973
- Deep Sensing of Urban Waterlogging S. Lo et al. 10.1109/ACCESS.2021.3111623
- Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al. 10.1016/j.jhydrol.2022.127726
- Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data J. Jiang et al. 10.3390/rs12061014
- Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
- Machine Learning and Urban Drainage Systems: State-of-the-Art Review S. Kwon & J. Kim 10.3390/w13243545
1 citations as recorded by crossref.
Latest update: 02 Oct 2023
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...
