Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., 1111 Budapest, Hungary
Gergely Benkő
Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., 1111 Budapest, Hungary
Sándor Baranya
Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., 1111 Budapest, Hungary
Viewed
Total article views: 2,189 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,640
480
69
2,189
67
94
HTML: 1,640
PDF: 480
XML: 69
Total: 2,189
BibTeX: 67
EndNote: 94
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads
(calculated since 14 Nov 2022)
Total article views: 1,251 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,013
191
47
1,251
52
81
HTML: 1,013
PDF: 191
XML: 47
Total: 1,251
BibTeX: 52
EndNote: 81
Views and downloads (calculated since 01 Nov 2023)
Cumulative views and downloads
(calculated since 01 Nov 2023)
Total article views: 938 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
627
289
22
938
15
13
HTML: 627
PDF: 289
XML: 22
Total: 938
BibTeX: 15
EndNote: 13
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads
(calculated since 14 Nov 2022)
Viewed (geographical distribution)
Total article views: 2,189 (including HTML, PDF, and XML)
Thereof 2,130 with geography defined
and 59 with unknown origin.
Total article views: 1,251 (including HTML, PDF, and XML)
Thereof 1,224 with geography defined
and 27 with unknown origin.
Total article views: 938 (including HTML, PDF, and XML)
Thereof 906 with geography defined
and 32 with unknown origin.
Country
#
Views
%
Country
#
Views
%
Country
#
Views
%
Total:
0
HTML:
0
PDF:
0
XML:
0
1
1
Total:
0
HTML:
0
PDF:
0
XML:
0
1
1
Total:
0
HTML:
0
PDF:
0
XML:
0
1
1
Latest update: 17 Sep 2025
Download
The requested paper has a corresponding corrigendum published.
Please read the corrigendum first before downloading the article.
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.
A novel, artificial-intelligence-based riverbed sediment analysis methodology is introduced that...