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 et al.
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21 citations as recorded by crossref.
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- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
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- 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
- 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
- Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
- Research on sports aided teaching and training decision system oriented to deep convolutional neural network Q. Mei & M. Li 10.3233/JIFS-219033
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- Deep Sensing of Urban Waterlogging S. Lo et al. 10.1109/ACCESS.2021.3111623
- 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
- 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
Latest update: 08 Aug 2022