Automated riverbed composition analysis using deep learning on underwater images

Ermilov, Alexander A.; Benkő, Gergely; Baranya, Sándor

doi:https://doi.org/10.5194/esurf-11-1061-2023

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

Data sets

Used dataset - Deep learning-based riverbed composition analysis from underwater images, Part 2 Alexander A. Ermilov, Gergely Benkő, and Sándor Baranya https://doi.org/10.6084/m9.figshare.23861385.v2

Used dataset - Deep learning-based riverbed composition analysis from underwater images, Part 1 Alexander A. Ermilov, Gergely Benkő, and Sándor Baranya https://doi.org/10.6084/m9.figshare.23876547.v1

Used dataset - Deep learning-based riverbed composition analysis from underwater images, Part 3 Alexander A. Ermilov, Gergely Benkő, and Sándor Baranya https://doi.org/10.6084/m9.figshare.23877951.v1

Model code and software

Source code - Deep learning-based riverbed composition analysis from underwater images Alexander A. Ermilov, Gergely Benkő, and Sándor Baranya https://doi.org/10.6084/m9.figshare.23860410.v1

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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.