Articles | Volume 10, issue 2
https://doi.org/10.5194/esurf-10-349-2022
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, Marwan A. Hassan, and Xudong Fu

Related authors

Coupling between downstream variations of channel width and local pool–riffle bed topography
Shawn M. Chartrand, A. Mark Jellinek, Marwan A. Hassan, and Carles Ferrer-Boix
Earth Surf. Dynam., 11, 1–20, https://doi.org/10.5194/esurf-11-1-2023,https://doi.org/10.5194/esurf-11-1-2023, 2023
Short summary
A combined approach of experimental and numerical modeling for 3D hydraulic features of a step-pool unit
Chendi Zhang, Yuncheng Xu, Marwan A. Hassan, Mengzhen Xu, and Pukang He
Earth Surf. Dynam., 10, 1253–1272, https://doi.org/10.5194/esurf-10-1253-2022,https://doi.org/10.5194/esurf-10-1253-2022, 2022
Short summary
Probabilistic description of bedload fluxes from the aggregate dynamics of individual grains
J. Kevin Pierce, Marwan A. Hassan, and Rui M. L. Ferreira
Earth Surf. Dynam., 10, 817–832, https://doi.org/10.5194/esurf-10-817-2022,https://doi.org/10.5194/esurf-10-817-2022, 2022
Short summary
Effect of stress history on sediment transport and channel adjustment in graded gravel-bed rivers
Chenge An, Marwan A. Hassan, Carles Ferrer-Boix, and Xudong Fu
Earth Surf. Dynam., 9, 333–350, https://doi.org/10.5194/esurf-9-333-2021,https://doi.org/10.5194/esurf-9-333-2021, 2021
Short summary
Characterization of morphological units in a small, forested stream using close-range remotely piloted aircraft imagery
Carina Helm, Marwan A. Hassan, and David Reid
Earth Surf. Dynam., 8, 913–929, https://doi.org/10.5194/esurf-8-913-2020,https://doi.org/10.5194/esurf-8-913-2020, 2020
Short summary

Related subject area

Physical: Geomorphology (including all aspects of fluvial, coastal, aeolian, hillslope and glacial geomorphology)
Bedload transport fluctuations, flow conditions, and disequilibrium ratio at the Swiss Erlenbach stream: results from 27 years of high-resolution temporal measurements
Dieter Rickenmann
Earth Surf. Dynam., 12, 11–34, https://doi.org/10.5194/esurf-12-11-2024,https://doi.org/10.5194/esurf-12-11-2024, 2024
Short summary
Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection
Byungho Kang, Rusty A. Feagin, Thomas Huff, and Orencio Durán Vinent
Earth Surf. Dynam., 12, 1–10, https://doi.org/10.5194/esurf-12-1-2024,https://doi.org/10.5194/esurf-12-1-2024, 2024
Short summary
Coexistence of two dune scales in a lowland river
Judith Y. Zomer, Bart Vermeulen, and Antonius J. F. Hoitink
Earth Surf. Dynam., 11, 1283–1298, https://doi.org/10.5194/esurf-11-1283-2023,https://doi.org/10.5194/esurf-11-1283-2023, 2023
Short summary
Alpine hillslope failure in the western US: insights from the Chaos Canyon landslide, Rocky Mountain National Park, USA
Matthew C. Morriss, Benjamin Lehmann, Benjamin Campforts, George Brencher, Brianna Rick, Leif S. Anderson, Alexander L. Handwerger, Irina Overeem, and Jeffrey Moore
Earth Surf. Dynam., 11, 1251–1274, https://doi.org/10.5194/esurf-11-1251-2023,https://doi.org/10.5194/esurf-11-1251-2023, 2023
Short summary
Using repeat UAV-based laser scanning and multispectral imagery to explore eco-geomorphic feedbacks along a river corridor
Christopher Tomsett and Julian Leyland
Earth Surf. Dynam., 11, 1223–1249, https://doi.org/10.5194/esurf-11-1223-2023,https://doi.org/10.5194/esurf-11-1223-2023, 2023
Short summary

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. 
Bunte, K. and Abt, S. R.: Sampling frame for Improving pebble Count Accuracy in Coarse Gravel-bed streams, J. Am. Water Resour., 37, 1001–1014, https://doi.org/10.1111/j.1752-1688.2001.tb05528.x, 2001. 
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. 
Download
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.