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.

Viewed

Total article views: 1,511 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,204 280 27 1,511 11 10
  • HTML: 1,204
  • PDF: 280
  • XML: 27
  • Total: 1,511
  • BibTeX: 11
  • EndNote: 10
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads (calculated since 15 Sep 2021)

Viewed (geographical distribution)

Total article views: 1,361 (including HTML, PDF, and XML) Thereof 1,361 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jun 2022
Download
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.