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
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1,647
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PDF: 336
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BibTeX: 34
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Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads
(calculated since 14 Nov 2022)
Total article views: 811 (including HTML, PDF, and XML)
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Views and downloads (calculated since 01 Nov 2023)
Cumulative views and downloads
(calculated since 01 Nov 2023)
Total article views: 836 (including HTML, PDF, and XML)
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836
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Total: 836
BibTeX: 11
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Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads
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Viewed (geographical distribution)
Total article views: 1,647 (including HTML, PDF, and XML)
Thereof 1,599 with geography defined
and 48 with unknown origin.
Total article views: 811 (including HTML, PDF, and XML)
Thereof 793 with geography defined
and 18 with unknown origin.
Total article views: 836 (including HTML, PDF, and XML)
Thereof 806 with geography defined
and 30 with unknown origin.
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Latest update: 15 Jan 2025
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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...
