61 citations as recorded by crossref.

1. ImageGrains 2.0: Improved precision and generalization for grain segmentation D. Mair et al. https://doi.org/10.5194/esurf-14-527-2026
2. Curvature-based pebble segmentation for reconstructed surface meshes A. Rheinwalt et al. https://doi.org/10.5194/esurf-13-923-2025
3. Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al. https://doi.org/10.5194/esurf-10-1211-2022
4. Multiband (X, C, L) radar amplitude analysis for a mixed sand- and gravel-bed river in the eastern Central Andes B. Purinton & B. Bookhagen https://doi.org/10.1016/j.rse.2020.111799
5. Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance J. Wiener & G. Pasternack https://doi.org/10.1016/j.geomorph.2022.108431
6. Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area C. Hiller et al. https://doi.org/10.1016/j.geomorph.2023.108857
7. 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. https://doi.org/10.1002/esp.5755
8. Grain size analysis in mountain rivers using UAV photogrammetry: proposal and validation of a D 50 homologation methodology M. Perez-Peralta et al. https://doi.org/10.1080/23863781.2025.2580710
9. A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches A. Soloy et al. https://doi.org/10.3390/rs12213659
10. Fine sediment dynamics over a gravel bar. Part 1: Validation of a new image-based segmentation method J. Deng et al. https://doi.org/10.1016/j.catena.2023.106978
11. Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes B. Purinton & B. Bookhagen https://doi.org/10.1029/2021JF006260
12. Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning Z. Chen et al. https://doi.org/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. https://doi.org/10.2139/ssrn.4181123
14. Integration of ERT and Geotechnical Investigation for River Restoration: A Case Study of Dam Removal Site Characterization M. Rahimi et al. https://doi.org/10.1007/s00267-026-02382-8
15. Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu https://doi.org/10.1016/j.measurement.2023.113386
16. CAGEY (CArbonate Grain Estimation with YOLO): Object detection for grain size, roundness, and dunham classification B. Liu et al. https://doi.org/10.1016/j.acags.2026.100329
17. The gravel-sand transition and grain size gap in river bed sediments E. Dingle et al. https://doi.org/10.1016/j.earscirev.2021.103838
18. An extrapolation algorithm for estimating river bed grain size distributions across basins J. Gilbert https://doi.org/10.5194/esurf-13-1307-2025
19. Constructing vertical measurement logs using UAV-based photogrammetry: Applications for multiscale high-resolution analysis of coarse-grained volcaniclastic stratigraphy Z. Smith & D. Maxwell https://doi.org/10.1016/j.jvolgeores.2020.107122
20. Remotely sensed rivers in the Anthropocene: state of the art and prospects H. Piégay et al. https://doi.org/10.1002/esp.4787
21. Image restoration of sediment particles in turbid environments based on a multi-scale fusion algorithm X. Ma et al. https://doi.org/10.1007/s11368-026-04330-9
22. Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods R. Miazza et al. https://doi.org/10.1002/esp.5782
23. 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. https://doi.org/10.1002/rra.3910
24. Automated riverbed composition analysis using deep learning on underwater images A. Ermilov et al. https://doi.org/10.5194/esurf-11-1061-2023
25. Adopting deep learning methods for airborne RGB fluvial scene classification P. Carbonneau et al. https://doi.org/10.1016/j.rse.2020.112107
26. Short-term geomorphic adjustments of bars in the Elbe, a large regulated river in Czechia T. Galia et al. https://doi.org/10.14712/23361980.2024.7
27. Revisiting the automated grain sizing technique (AGS) for characterizing grain size distribution M. Sulaiman et al. https://doi.org/10.1080/15715124.2021.1917585
28. GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks N. Lang et al. https://doi.org/10.5194/hess-25-2567-2021
29. Mapping riverbed sediment size from Sentinel‐2 satellite data G. Marchetti et al. https://doi.org/10.1002/esp.5394
30. Manifestations of the long-term transformation of the lower Elbe channel in Czechia and opportunities for its restoration J. Hradecký et al. https://doi.org/10.37040/geografie.2024.012
31. Automatic grain-size curve analyses and unconventional determination of the volume of the muck from TBM through photogrammetry and apple LiDAR sensor A. Lingua et al. https://doi.org/10.1007/s12518-026-00717-y
32. Inferring Dynamic Fragmentation Through the Particle Size and Shape Distribution of a Rock Avalanche K. Jin et al. https://doi.org/10.1029/2022JF006784
33. Dynamic Characteristics and Risk Assessment of the Yiziyan Rock Topples in Jinsha County, Guizhou, China Y. Zhang et al. https://doi.org/10.1007/s00603-024-03935-1
34. Different methods of estimating riverbed sediment grain size diverge at the basin scale P. Regier et al. https://doi.org/10.3389/feart.2025.1529503
35. The influence of grain size sorting on the roughness parametrization of gravel riverbeds A. do Prado et al. https://doi.org/10.1016/j.geomorph.2024.109565
36. A Scalable Open Source Workflow for Riverbed Substrate Classification Using UAV Imagery T. Soto Parra et al. https://doi.org/10.3390/rs18152529
37. Multitype sediment deposits and sediment supply potential across alluvial–bedrock reaches of the Parlung Tsangpo river R. Li et al. https://doi.org/10.1016/j.ijsrc.2026.07.003
38. A Field Imagery and Measurement Dataset for Grain-size Analysis of Riverbed Gravels in the Mid-Geum River H. Yoon et al. https://doi.org/10.22761/GD.2025.0042
39. Spatial Distribution Characteristics of Rock Avalanche Fragments From Numerical Analysis and UAV Image Recognition W. Chang et al. https://doi.org/10.1007/s00603-025-04510-y
40. Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al. https://doi.org/10.1016/j.sedgeo.2023.106340
41. Grain size from source to sink – modern and ancient fining rates T. Reynolds https://doi.org/10.1016/j.earscirev.2024.104699
42. Distributed estimation of surface sediment size in paraglacial and periglacial environments using drone photogrammetry G. Zegers et al. https://doi.org/10.1002/esp.70093
43. An Integrated Framework for the Assessment of Meso‐Scale Physical Habitats in Gravel‐Bed Rivers Using Remote Sensing and 2D Hydraulic Modeling D. Farò et al. https://doi.org/10.1002/wat2.70027
44. Bed material facies mapping at braided river scale and evidence for trends in fine sediment J. Rogers et al. https://doi.org/10.1002/esp.70012
45. River surface sediment grain size and sorting changes after several experimental floods in the lower Spöl River (Switzerland) V. Ruiz-Villanueva et al. https://doi.org/10.1007/s11368-026-04490-8
46. FastGAS: a UAV-Enabled framework for fast and robust gravel auto-sieving in coastal and mountainous fluvial environments S. Gao et al. https://doi.org/10.1016/j.jhydrol.2025.133937
47. Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. https://doi.org/10.5194/esurf-10-953-2022
48. Spatial distribution and transport characteristics of debris flow sediment using high resolution UAV images in the Ohya debris flow fan S. Yousefi et al. https://doi.org/10.1016/j.geomorph.2024.109533
49. Coarse sediment grain size variability along gravel-bed rivers via automatic grain size detection (a case study of the Ondava River, Slovakia) A. MD et al. https://doi.org/10.1080/19475705.2025.2582752
50. Verification of the Manning’s Roughness Coefficient of Fish Pass Riverbeds Using Drone-Based Photogrammetry L. Čubanová et al. https://doi.org/10.3390/w17101409
51. Automatic Segmentation of Individual Grains From a Terrestrial Laser Scanning Point Cloud of a Mountain River Bed A. Walicka & N. Pfeifer https://doi.org/10.1109/JSTARS.2022.3141892
52. Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images H. Yoo et al. https://doi.org/10.3390/jmse12010172
53. Gravel automatic sieving method fusing macroscopic and microscopic characteristics S. Gao et al. https://doi.org/10.1016/j.ijsrc.2024.05.002
54. Automated mapping of the mean particle diameter characteristics from UAV-imagery using the CNN-based GRAINet model T. Lendzioch et al. https://doi.org/10.2166/hydro.2023.079
55. Investigation of deposition characteristics using a novel super-resolution method: a case study of Baiyan rock avalanche in Guizhou, China J. He et al. https://doi.org/10.1007/s10346-025-02512-z
56. Downstream rounding rate of pebbles in the Himalaya P. Pokhrel et al. https://doi.org/10.5194/esurf-12-515-2024
57. OrthoSAM: multi-scale extension of the Segment Anything Model for river pebble delineation from large orthophotos V. Chan et al. https://doi.org/10.5194/esurf-14-391-2026
58. FKgrain: A topography-based software tool for grain segmentation and sizing using factorial kriging F. Wu et al. https://doi.org/10.1007/s12145-021-00660-z
59. Investigating controls on fluvial grain sizes in post-glacial landscapes using citizen science A. Towers et al. https://doi.org/10.5194/esurf-14-95-2026
60. Plane morphometric analysis of particles using an automatic image analysis system: a case study of the Xinmo landslide K. Jin et al. https://doi.org/10.1007/s10035-023-01375-2
61. Characterizing coarse sediment grain size variability along the upper Sandy River, Oregon, via UAV remote sensing E. Levenson & M. Fonstad https://doi.org/10.1016/j.geomorph.2022.108447