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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 May 2021
Research article |
| 19 May 2021
| 19 May 2021
GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zurich, Switzerland
Andrea Irniger
CORRESPONDING AUTHOR
Hunziker, Zarn & Partner, Aarau, Switzerland
Agnieszka Rozniak
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zurich, Switzerland
Roni Hunziker
Hunziker, Zarn & Partner, Aarau, Switzerland
Jan Dirk Wegner
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zurich, Switzerland
Konrad Schindler
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zürich, Zurich, Switzerland
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Cited
22 citations as recorded by crossref.
- Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. 10.5194/esurf-10-953-2022
- Spoil characterisation using UAV-based optical remote sensing in coal mine dumps S. Thiruchittampalam et al. 10.1007/s40789-023-00622-4
- Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods R. Miazza et al. 10.1002/esp.5782
- FKgrain: A topography-based software tool for grain segmentation and sizing using factorial kriging F. Wu et al. 10.1007/s12145-021-00660-z
- Remote Sensing of Riparian Ecosystems M. Rusnák et al. 10.3390/rs14112645
- Gravel automatic sieving method fusing macroscopic and microscopic characteristics S. Gao et al. 10.1016/j.ijsrc.2024.05.002
- Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning Z. Chen et al. 10.1002/esp.5545
- Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields A. Bayle et al. 10.5194/bg-20-1649-2023
- Evolution of rock heterogeneity under coupled stress-temperature gradient loading and implications for underground cryogenic storage D. Lu & W. Wu 10.1016/j.est.2023.109519
- STUDY ON THE APPLICATION OF THE MACHINE LEARNING MODEL 'GRAINet' TO A GRAVEL BEACH AND CHANGES IN GRAIN SIZE M. KIKU et al. 10.2208/jscejj.24-18162
- Measuring the grain‐size distributions of mass movement deposits E. Harvey et al. 10.1002/esp.5337
- Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area C. Hiller et al. 10.1016/j.geomorph.2023.108857
- Investigation of Flow Characteristics of Landslide Materials Through Pore Space Topology and Complex Network Analysis J. Zhang et al. 10.1029/2021WR031735
- Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu 10.1016/j.measurement.2023.113386
- Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River V. Chardon et al. 10.1002/rra.3910
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. 10.1002/esp.5755
- Vision-based monitoring of railway superstructure: A review P. Aela et al. 10.1016/j.conbuildmat.2024.137385
- Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images H. Yoo et al. 10.3390/jmse12010172
- Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset X. Chen et al. 10.5194/esurf-10-349-2022
- Mapping riverbed sediment size from Sentinel‐2 satellite data G. Marchetti et al. 10.1002/esp.5394
- Automated mapping of the mean particle diameter characteristics from UAV-imagery using the CNN-based GRAINet model T. Lendzioch et al. 10.2166/hydro.2023.079
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
22 citations as recorded by crossref.
- Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. 10.5194/esurf-10-953-2022
- Spoil characterisation using UAV-based optical remote sensing in coal mine dumps S. Thiruchittampalam et al. 10.1007/s40789-023-00622-4
- Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods R. Miazza et al. 10.1002/esp.5782
- FKgrain: A topography-based software tool for grain segmentation and sizing using factorial kriging F. Wu et al. 10.1007/s12145-021-00660-z
- Remote Sensing of Riparian Ecosystems M. Rusnák et al. 10.3390/rs14112645
- Gravel automatic sieving method fusing macroscopic and microscopic characteristics S. Gao et al. 10.1016/j.ijsrc.2024.05.002
- Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning Z. Chen et al. 10.1002/esp.5545
- Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields A. Bayle et al. 10.5194/bg-20-1649-2023
- Evolution of rock heterogeneity under coupled stress-temperature gradient loading and implications for underground cryogenic storage D. Lu & W. Wu 10.1016/j.est.2023.109519
- STUDY ON THE APPLICATION OF THE MACHINE LEARNING MODEL 'GRAINet' TO A GRAVEL BEACH AND CHANGES IN GRAIN SIZE M. KIKU et al. 10.2208/jscejj.24-18162
- Measuring the grain‐size distributions of mass movement deposits E. Harvey et al. 10.1002/esp.5337
- Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area C. Hiller et al. 10.1016/j.geomorph.2023.108857
- Investigation of Flow Characteristics of Landslide Materials Through Pore Space Topology and Complex Network Analysis J. Zhang et al. 10.1029/2021WR031735
- Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu 10.1016/j.measurement.2023.113386
- Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River V. Chardon et al. 10.1002/rra.3910
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. 10.1002/esp.5755
- Vision-based monitoring of railway superstructure: A review P. Aela et al. 10.1016/j.conbuildmat.2024.137385
- Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images H. Yoo et al. 10.3390/jmse12010172
- Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset X. Chen et al. 10.5194/esurf-10-349-2022
- Mapping riverbed sediment size from Sentinel‐2 satellite data G. Marchetti et al. 10.1002/esp.5394
- Automated mapping of the mean particle diameter characteristics from UAV-imagery using the CNN-based GRAINet model T. Lendzioch et al. 10.2166/hydro.2023.079
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
Latest update: 16 Nov 2024
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.
Grain size analysis is the key to understanding the sediment dynamics of river systems and is an...
