40 citations as recorded by crossref.

1. Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al. 10.5194/esurf-10-1211-2022
2. Multiband (X, C, L) radar amplitude analysis for a mixed sand- and gravel-bed river in the eastern Central Andes B. Purinton & B. Bookhagen 10.1016/j.rse.2020.111799
3. The influence of grain size sorting on the roughness parametrization of gravel riverbeds A. do Prado et al. 10.1016/j.geomorph.2024.109565
4. Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance J. Wiener & G. Pasternack 10.1016/j.geomorph.2022.108431
5. 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
6. 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
7. A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches A. Soloy et al. 10.3390/rs12213659
8. Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al. 10.1016/j.sedgeo.2023.106340
9. Fine sediment dynamics over a gravel bar. Part 1: Validation of a new image-based segmentation method J. Deng et al. 10.1016/j.catena.2023.106978
10. Grain size from source to sink – modern and ancient fining rates T. Reynolds 10.1016/j.earscirev.2024.104699
11. Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes B. Purinton & B. Bookhagen 10.1029/2021JF006260
12. Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning Z. Chen et al. 10.1002/esp.5545
13. Fine Sediment Dynamics Over a Gravel Bar. Part 1: Validation of a New Image-Based Segmentation Method J. Deng et al. 10.2139/ssrn.4181123
14. Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu 10.1016/j.measurement.2023.113386
15. The gravel-sand transition and grain size gap in river bed sediments E. Dingle et al. 10.1016/j.earscirev.2021.103838
16. Constructing vertical measurement logs using UAV-based photogrammetry: Applications for multiscale high-resolution analysis of coarse-grained volcaniclastic stratigraphy Z. Smith & D. Maxwell 10.1016/j.jvolgeores.2020.107122
17. Remotely sensed rivers in the Anthropocene: state of the art and prospects H. Piégay et al. 10.1002/esp.4787
18. 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
19. Spatial distribution and transport characteristics of debris flow sediment using high resolution UAV images in the Ohya debris flow fan S. Yousefi et al. 10.1016/j.geomorph.2024.109533
20. Automatic Segmentation of Individual Grains From a Terrestrial Laser Scanning Point Cloud of a Mountain River Bed A. Walicka & N. Pfeifer 10.1109/JSTARS.2022.3141892
21. Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images H. Yoo et al. 10.3390/jmse12010172
22. Gravel automatic sieving method fusing macroscopic and microscopic characteristics S. Gao et al. 10.1016/j.ijsrc.2024.05.002
23. 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
24. 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
25. 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
26. Automated riverbed composition analysis using deep learning on underwater images A. Ermilov et al. 10.5194/esurf-11-1061-2023
27. Downstream rounding rate of pebbles in the Himalaya P. Pokhrel et al. 10.5194/esurf-12-515-2024
28. Adopting deep learning methods for airborne RGB fluvial scene classification P. Carbonneau et al. 10.1016/j.rse.2020.112107
29. Short-term geomorphic adjustments of bars in the Elbe, a large regulated river in Czechia T. Galia et al. 10.14712/23361980.2024.7
30. Revisiting the automated grain sizing technique (AGS) for characterizing grain size distribution M. Sulaiman et al. 10.1080/15715124.2021.1917585
31. GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks N. Lang et al. 10.5194/hess-25-2567-2021
32. Mapping riverbed sediment size from Sentinel‐2 satellite data G. Marchetti et al. 10.1002/esp.5394
33. Manifestations of the long-term transformation of the lower Elbe channel in Czechia and opportunities for its restoration J. Hradecký et al. 10.37040/geografie.2024.012
34. FKgrain: A topography-based software tool for grain segmentation and sizing using factorial kriging F. Wu et al. 10.1007/s12145-021-00660-z
35. Inferring Dynamic Fragmentation Through the Particle Size and Shape Distribution of a Rock Avalanche K. Jin et al. 10.1029/2022JF006784
36. Plane morphometric analysis of particles using an automatic image analysis system: a case study of the Xinmo landslide K. Jin et al. 10.1007/s10035-023-01375-2
37. Dynamic Characteristics and Risk Assessment of the Yiziyan Rock Topples in Jinsha County, Guizhou, China Y. Zhang et al. 10.1007/s00603-024-03935-1
38. Characterizing coarse sediment grain size variability along the upper Sandy River, Oregon, via UAV remote sensing E. Levenson & M. Fonstad 10.1016/j.geomorph.2022.108447
39. Measuring the grain‐size distributions of mass movement deposits E. Harvey et al. 10.1002/esp.5337
40. Improving predictions of critical shear stress in gravel bed rivers: Identifying the onset of sediment transport and quantifying sediment structure R. Hodge et al. 10.1002/esp.5842