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

Vandaele, Remy; Dance, Sarah L.; Ojha, Varun

doi:https://doi.org/10.5194/hess-25-4435-2021

Articles | Volume 25, issue 8

https://doi.org/10.5194/hess-25-4435-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-4435-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 8

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, Sarah L. Dance, and Varun Ojha

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

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.