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: 2,970 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,224
652
94
2,970
99
144
HTML: 2,224
PDF: 652
XML: 94
Total: 2,970
BibTeX: 99
EndNote: 144
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads
(calculated since 14 Nov 2022)
Total article views: 1,872 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,526
277
69
1,872
79
123
HTML: 1,526
PDF: 277
XML: 69
Total: 1,872
BibTeX: 79
EndNote: 123
Views and downloads (calculated since 01 Nov 2023)
Cumulative views and downloads
(calculated since 01 Nov 2023)
Total article views: 1,098 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
698
375
25
1,098
20
21
HTML: 698
PDF: 375
XML: 25
Total: 1,098
BibTeX: 20
EndNote: 21
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads
(calculated since 14 Nov 2022)
Viewed (geographical distribution)
Total article views: 2,970 (including HTML, PDF, and XML)
Thereof 2,900 with geography defined
and 70 with unknown origin.
Total article views: 1,872 (including HTML, PDF, and XML)
Thereof 1,836 with geography defined
and 36 with unknown origin.
Total article views: 1,098 (including HTML, PDF, and XML)
Thereof 1,064 with geography defined
and 34 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: 23 Apr 2026
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...
