Articles | Volume 25, issue 8
Hydrol. Earth Syst. Sci., 25, 4435–4453, 2021
https://doi.org/10.5194/hess-25-4435-2021
Hydrol. Earth Syst. Sci., 25, 4435–4453, 2021
https://doi.org/10.5194/hess-25-4435-2021

Research article 16 Aug 2021

Research article | 16 Aug 2021

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

Remy Vandaele et al.

Data sets

Deep learning for the estimation of water-levels using river cameras: networks and datasets R. Vandaele, S. L. Dance, and V. Ojha https://doi.org/10.17864/1947.282

River water level height measurements obtained from river cameras near Tewkesbury S. Vetra-Carvalho, S. L. Dance, D. Mason, J. Waller, P. Smith, J. Tabeart, and E. Cooper https://doi.org/10.17632/769cyvdznp.1

Model code and software

Deep learning for the estimation of water-levels using river cameras: networks and datasets R. Vandaele, S. L. Dance, and V. Ojha https://doi.org/10.17864/1947.282

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Short summary
The acquisition of river-level data is a critical task for the understanding of flood events but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes applying deep learning techniques on river-camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.