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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-196
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-196
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 May 2020

25 May 2020

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A revised version of this preprint is currently under review for the journal HESS.

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

Nico Lang1, Andrea Irniger2, Agnieszka Rozniak1, Roni Hunziker2, Jan Dirk Wegner1, and Konrad Schindler1 Nico Lang et al.
  • 1EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Switzerland
  • 2Hunziker, Zarn & Partner, Aarau, Switzerland

Abstract. Grain size analysis is the key to understand the sediment dynamics of river systems. We propose GRAINet, a data-driven approach to analyze grain size distributions of entire gravel bars based on georeferenced UAV images. A convolutional neural network is trained to regress grain size distributions as well as the characteristic mean diameter from raw images. GRAINet allows the holistic analysis of entire gravel bars, resulting in (i) high-resolution maps of the spatial grain size distribution at large scale, and (ii) robust grading curves for entire gravel bars. To collect a large training dataset of 1,491 samples, we introduce digital line sampling as a new annotation strategy, following the widely applied line sampling analysis of Fehr (1987). Our evaluation on 25 gravel bars along 6 different rivers in Switzerland yields high accuracy: The resulting maps of mean diameters have a mean absolute error of 1.1 cm, with no bias. Robust grading curves for entire gravel bars can be extracted if representative training data is available. At the gravel bar level the MAE error of the predicted mean diameter is even reduced to 0.3 cm. Extensive experiments were carried out to study the quality of the digital line samples, the generalization capability of GRAINet to new locations, the model performance w.r.t. human labeling noise, the limitations of the current model, and the potential of GRAINet to analyze images with low resolutions.

Nico Lang et al.

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Nico Lang et al.

Nico Lang et al.

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Latest update: 26 Nov 2020
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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 a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.
Grain size analysis is the key to understanding the sediment dynamics of river systems and is an...
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