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.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-20', Kenneth Chapman, 19 Feb 2021
    • AC1: 'Reply on RC1', Remy Vandaele, 08 Mar 2021
  • RC2: 'Comment on hess-2021-20', Anonymous Referee #2, 01 Mar 2021
    • AC2: 'Reply on RC2', Remy Vandaele, 23 Mar 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (24 Apr 2021) by Jan Seibert
AR by Remy Vandaele on behalf of the Authors (24 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (03 Jun 2021) by Jan Seibert
RR by Ze Wang (23 Jun 2021)
ED: Publish subject to minor revisions (review by editor) (27 Jun 2021) by Jan Seibert
AR by Remy Vandaele on behalf of the Authors (02 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (10 Jul 2021) by Jan Seibert
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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.