Articles | Volume 10, issue 2
Earth Surf. Dynam., 10, 349–366, 2022
https://doi.org/10.5194/esurf-10-349-2022
Earth Surf. Dynam., 10, 349–366, 2022
https://doi.org/10.5194/esurf-10-349-2022
Research article
27 Apr 2022
Research article | 27 Apr 2022

Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset

Xingyu Chen 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 esurf-2021-67', Byungho Kang, 11 Dec 2021
    • AC2: 'Reply on RC1', Xingyu Chen, 21 Jan 2022
  • RC2: 'Comment on esurf-2021-67', Anonymous Referee #2, 15 Dec 2021
    • AC3: 'Reply on RC2', Xingyu Chen, 21 Jan 2022
  • AC1: 'Comment on esurf-2021-67', Xingyu Chen, 21 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Xingyu Chen on behalf of the Authors (03 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (15 Feb 2022) by Orencio Duran Vinent
RR by Anonymous Referee #2 (17 Feb 2022)
RR by Byungho Kang (17 Feb 2022)
ED: Publish subject to minor revisions (review by editor) (25 Feb 2022) by Orencio Duran Vinent
AR by Xingyu Chen on behalf of the Authors (06 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (08 Mar 2022) by Orencio Duran Vinent
ED: Publish subject to technical corrections (10 Mar 2022) by Niels Hovius(Editor)
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Short summary
We compiled a large image dataset containing more than 125 000 sediments and developed a model (GrainID) based on convolutional neural networks to measure individual grain size from images. The model was calibrated on flume and natural stream images covering a wide range of fluvial environments. The model showed high performance compared with other methods. Our model showed great potential for grain size measurements from a small patch of sediment in a flume to a watershed-scale drone survey.