Articles | Volume 25, issue 5
Hydrol. Earth Syst. Sci., 25, 2567–2597, 2021
https://doi.org/10.5194/hess-25-2567-2021
Hydrol. Earth Syst. Sci., 25, 2567–2597, 2021
https://doi.org/10.5194/hess-25-2567-2021

Research article 19 May 2021

Research article | 19 May 2021

GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks

Nico Lang et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (22 Oct 2020) by Matjaz Mikos
AR by Nico Lang on behalf of the Authors (25 Nov 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (27 Nov 2020) by Matjaz Mikos
RR by Patrice Carbonneau (20 Dec 2020)
RR by Anonymous Referee #3 (30 Dec 2020)
ED: Reconsider after major revisions (further review by editor and referees) (31 Dec 2020) by Matjaz Mikos
AR by Nico Lang on behalf of the Authors (05 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (08 Feb 2021) by Matjaz Mikos
RR by Patrice Carbonneau (10 Mar 2021)
ED: Publish as is (25 Mar 2021) by Matjaz Mikos
AR by Nico Lang on behalf of the Authors (29 Mar 2021)  Author's response    Manuscript
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Short summary
Grain size analysis is the key to understanding the sediment dynamics of river systems and is an important indicator for mitigating flood risk and preserving biodiversity in aquatic habitats. We propose GRAINet, a data-driven approach based on deep learning, to regress grain size distributions from georeferenced UAV images. This allows for a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.