Articles | Volume 25, issue 5
Hydrol. Earth Syst. Sci., 25, 2567–2597, 2021
https://doi.org/10.5194/hess-25-2567-2021
Hydrol. Earth Syst. Sci., 25, 2567–2597, 2021
https://doi.org/10.5194/hess-25-2567-2021

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 et al.

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Cited articles

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Short summary
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.