Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 10, issue 2
Articles | Volume 10, issue 2
https://doi.org/10.5194/esurf-10-349-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-10-349-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 2
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, Marwan A. Hassan, and Xudong Fu

Viewed

Total article views: 4,283 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,136 1,052 95 4,283 99 119
  • HTML: 3,136
  • PDF: 1,052
  • XML: 95
  • Total: 4,283
  • BibTeX: 99
  • EndNote: 119
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads (calculated since 15 Sep 2021)

Viewed (geographical distribution)

Total article views: 4,283 (including HTML, PDF, and XML) Thereof 4,057 with geography defined and 226 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Dec 2025
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.
Read more
Share