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

Related authors

Interactive Snow Avalanche Segmentation from Webcam Imagery: results, potential and limitations
Elisabeth Doris Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
EGUsphere, https://doi.org/10.5194/egusphere-2024-498,https://doi.org/10.5194/egusphere-2024-498, 2024
Short summary
TOWARDS ACCURATE INSTANCE SEGMENTATION IN LARGE-SCALE LIDAR POINT CLOUDS
B. Xiang, T. Peters, T. Kontogianni, F. Vetterli, S. Puliti, R. Astrup, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 605–612, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023,https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, 2023
Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023,https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
FOREST FIRE SUSCEPTIBILITY ASSESSMENT WITH MACHINE LEARNING METHODS IN NORTH-EAST TURKIYE
O. Kantarcioglu, K. Schindler, and S. Kocaman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 161–167, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023,https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, 2023
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022,https://doi.org/10.5194/tc-16-3517-2022, 2022
Short summary
More articles (7)

Related subject area

Subject: Rivers and Lakes | Techniques and Approaches: Mathematical applications
A wavelet-based approach to streamflow event identification and modeled timing error evaluation
Erin Towler and James L. McCreight
Hydrol. Earth Syst. Sci., 25, 2599–2615, https://doi.org/10.5194/hess-25-2599-2021,https://doi.org/10.5194/hess-25-2599-2021, 2021
Short summary
Variability in epilimnion depth estimations in lakes
Harriet L. Wilson, Ana I. Ayala, Ian D. Jones, Alec Rolston, Don Pierson, Elvira de Eyto, Hans-Peter Grossart, Marie-Elodie Perga, R. Iestyn Woolway, and Eleanor Jennings
Hydrol. Earth Syst. Sci., 24, 5559–5577, https://doi.org/10.5194/hess-24-5559-2020,https://doi.org/10.5194/hess-24-5559-2020, 2020
Short summary
Hydrodynamic and environmental characteristics of a tributary bay influenced by backwater jacking and intrusions from a main reservoir
Xintong Li, Bing Liu, Yuanming Wang, Yongan Yang, Ruifeng Liang, Fangjun Peng, Shudan Xue, Zaixiang Zhu, and Kefeng Li
Hydrol. Earth Syst. Sci., 24, 5057–5076, https://doi.org/10.5194/hess-24-5057-2020,https://doi.org/10.5194/hess-24-5057-2020, 2020
Short summary
Automatic identification of alternating morphological units in river channels using wavelet analysis and ridge extraction
Mounir Mahdade, Nicolas Le Moine, Roger Moussa, Oldrich Navratil, and Pierre Ribstein
Hydrol. Earth Syst. Sci., 24, 3513–3537, https://doi.org/10.5194/hess-24-3513-2020,https://doi.org/10.5194/hess-24-3513-2020, 2020
Short summary
Stream temperature and discharge evolution in Switzerland over the last 50 years: annual and seasonal behaviour
Adrien Michel, Tristan Brauchli, Michael Lehning, Bettina Schaefli, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 24, 115–142, https://doi.org/10.5194/hess-24-115-2020,https://doi.org/10.5194/hess-24-115-2020, 2020
Short summary
More articles (8)

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
More articles (52)
Download
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.
Read more