Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 11, issue 6
Articles | Volume 11, issue 6
https://doi.org/10.5194/esurf-11-1061-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-11-1061-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 6
Research article
 | 
01 Nov 2023
Research article |  | 01 Nov 2023

Automated riverbed composition analysis using deep learning on underwater images

Alexander A. Ermilov, Gergely Benkő, and Sándor Baranya

Viewed

Total article views: 1,125 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
847 243 35 1,125 22 22
  • HTML: 847
  • PDF: 243
  • XML: 35
  • Total: 1,125
  • BibTeX: 22
  • EndNote: 22
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads (calculated since 14 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,125 (including HTML, PDF, and XML) Thereof 1,101 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 May 2024
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
A novel, artificial-intelligence-based riverbed sediment analysis methodology is introduced that uses underwater images to identify the characteristic sediment classes. The main novelties of the procedure are as follows: underwater images are used, the method enables continuous mapping of the riverbed along the measurement vessel’s route contrary to conventional techniques, the method is cost-efficient, and the method works without scaling.
Read more