Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 5
Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2567-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2567-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 5
Research article
 | 
19 May 2021
Research article |  | 19 May 2021

GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks

Nico Lang, Andrea Irniger, Agnieszka Rozniak, Roni Hunziker, Jan Dirk Wegner, and Konrad Schindler

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Cited articles

Adams, J.: Gravel size analysis from photographs, J. Hydr. Eng. Div.-ASCE, 105, 1247–1255, 1979. a
Babej, J., Máčka, Z., Ondrejka, P., and Peterová, P.: Surface grain size variation within gravel bars: a case study of the River Opava, Czech Republic, Geogr. Fis. Dinam. Quatern., 39, 3–12, 2016. a
Badoux, A., Andres, N., and Turowski, J. M.: Damage costs due to bedload transport processes in Switzerland, Nat. Hazards Earth Syst. Sci., 14, 279–294, https://doi.org/10.5194/nhess-14-279-2014, 2014. a
Black, M., Carbonneau, P., Church, M., and Warburton, J.: Mapping sub-pixel fluvial grain sizes with hyperspatial imagery, Sedimentology, 61, 691–711, 2014. a, b
Brasington, J., Vericat, D., and Rychkov, I.: Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning, Water Resour. Res., 48, W11519, https://doi.org/10.1029/2012WR012223, 2012. a
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Grain size analysis is the key to understanding the sediment dynamics of river systems and is an important indicator for mitigating flood risk and preserving biodiversity in aquatic habitats. We propose GRAINet, a data-driven approach based on deep learning, to regress grain size distributions from georeferenced UAV images. This allows for a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.
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