Articles | Volume 10, issue 2
Earth Surf. Dynam., 10, 349–366, 2022
https://doi.org/10.5194/esurf-10-349-2022
Earth Surf. Dynam., 10, 349–366, 2022
https://doi.org/10.5194/esurf-10-349-2022
Research article
27 Apr 2022
Research article | 27 Apr 2022

Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset

Xingyu Chen et al.

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Cited articles

Adams, J.: Gravel Size Analysis from Photographs, J. Hydr. Eng. Div.-ASCE, 105, 1247–1255, https://doi.org/10.1061/JYCEAJ.0005283, 1979. 
An, C., Hassan, M. A., Ferrer-Boix, C., and Fu, X.: Effect of stress history on sediment transport and channel adjustment in graded gravel-bed rivers, Earth Surf. Dynam., 9, 333–350, https://doi.org/10.5194/esurf-9-333-2021, 2021. 
Brayshaw, D.: Bankfull and effective discharge in small mountain streams of British Columbia, The University of British Columbia, Vancouver, Canada, 70–71, https://doi.org/10.14288/1.0072555, 2012. 
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Buscombe, D.: SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry, Earth Surf. Proc. Land., 45, 638–651, https://doi.org/10.1002/esp.4760, 2020. 
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Short summary
We compiled a large image dataset containing more than 125 000 sediments and developed a model (GrainID) based on convolutional neural networks to measure individual grain size from images. The model was calibrated on flume and natural stream images covering a wide range of fluvial environments. The model showed high performance compared with other methods. Our model showed great potential for grain size measurements from a small patch of sediment in a flume to a watershed-scale drone survey.