56 citations as recorded by crossref.

1. Curvature-based pebble segmentation for reconstructed surface meshes A. Rheinwalt et al.
2. Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al.
3. Multiband (X, C, L) radar amplitude analysis for a mixed sand- and gravel-bed river in the eastern Central Andes B. Purinton & B. Bookhagen
4. Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance J. Wiener & G. Pasternack
5. Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area C. Hiller et al.
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.
7. Grain size analysis in mountain rivers using UAV photogrammetry: proposal and validation of a D 50 homologation methodology M. Perez-Peralta et al.
8. A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches A. Soloy et al.
9. Fine sediment dynamics over a gravel bar. Part 1: Validation of a new image-based segmentation method J. Deng et al.
10. Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes B. Purinton & B. Bookhagen
11. Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning Z. Chen et al.
12. Fine Sediment Dynamics Over a Gravel Bar. Part 1: Validation of a New Image-Based Segmentation Method J. Deng et al.
13. Integration of ERT and Geotechnical Investigation for River Restoration: A Case Study of Dam Removal Site Characterization M. Rahimi et al.
14. Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu
15. CAGEY (CArbonate Grain Estimation with YOLO): Object detection for grain size, roundness, and dunham classification B. Liu et al.
16. The gravel-sand transition and grain size gap in river bed sediments E. Dingle et al.
17. An extrapolation algorithm for estimating river bed grain size distributions across basins J. Gilbert
18. Constructing vertical measurement logs using UAV-based photogrammetry: Applications for multiscale high-resolution analysis of coarse-grained volcaniclastic stratigraphy Z. Smith & D. Maxwell
19. Remotely sensed rivers in the Anthropocene: state of the art and prospects H. Piégay et al.
20. Image restoration of sediment particles in turbid environments based on a multi-scale fusion algorithm X. Ma et al.
21. Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods R. Miazza et al.
22. 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.
23. Automated riverbed composition analysis using deep learning on underwater images A. Ermilov et al.
24. Adopting deep learning methods for airborne RGB fluvial scene classification P. Carbonneau et al.
25. Short-term geomorphic adjustments of bars in the Elbe, a large regulated river in Czechia T. Galia et al.
26. Revisiting the automated grain sizing technique (AGS) for characterizing grain size distribution M. Sulaiman et al.
27. GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks N. Lang et al.
28. Mapping riverbed sediment size from Sentinel‐2 satellite data G. Marchetti et al.
29. Manifestations of the long-term transformation of the lower Elbe channel in Czechia and opportunities for its restoration J. Hradecký et al.
30. 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.
31. Inferring Dynamic Fragmentation Through the Particle Size and Shape Distribution of a Rock Avalanche K. Jin et al.
32. Dynamic Characteristics and Risk Assessment of the Yiziyan Rock Topples in Jinsha County, Guizhou, China Y. Zhang et al.
33. Different methods of estimating riverbed sediment grain size diverge at the basin scale P. Regier et al.
34. The influence of grain size sorting on the roughness parametrization of gravel riverbeds A. do Prado et al.
35. A Field Imagery and Measurement Dataset for Grain-size Analysis of Riverbed Gravels in the Mid-Geum River H. Yoon et al.
36. Spatial Distribution Characteristics of Rock Avalanche Fragments From Numerical Analysis and UAV Image Recognition W. Chang et al.
37. Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al.
38. Grain size from source to sink – modern and ancient fining rates T. Reynolds
39. Distributed estimation of surface sediment size in paraglacial and periglacial environments using drone photogrammetry G. Zegers et al.
40. 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.
41. Bed material facies mapping at braided river scale and evidence for trends in fine sediment J. Rogers et al.
42. FastGAS: a UAV-Enabled framework for fast and robust gravel auto-sieving in coastal and mountainous fluvial environments S. Gao et al.
43. Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al.
44. Spatial distribution and transport characteristics of debris flow sediment using high resolution UAV images in the Ohya debris flow fan S. Yousefi et al.
45. 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.
46. Verification of the Manning’s Roughness Coefficient of Fish Pass Riverbeds Using Drone-Based Photogrammetry L. Čubanová et al.
47. Automatic Segmentation of Individual Grains From a Terrestrial Laser Scanning Point Cloud of a Mountain River Bed A. Walicka & N. Pfeifer
48. Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images H. Yoo et al.
49. Gravel automatic sieving method fusing macroscopic and microscopic characteristics S. Gao et al.
50. Automated mapping of the mean particle diameter characteristics from UAV-imagery using the CNN-based GRAINet model T. Lendzioch et al.
51. Investigation of deposition characteristics using a novel super-resolution method: a case study of Baiyan rock avalanche in Guizhou, China J. He et al.
52. Downstream rounding rate of pebbles in the Himalaya P. Pokhrel et al.
53. FKgrain: A topography-based software tool for grain segmentation and sizing using factorial kriging F. Wu et al.
54. Investigating controls on fluvial grain sizes in post-glacial landscapes using citizen science A. Towers et al.
55. Plane morphometric analysis of particles using an automatic image analysis system: a case study of the Xinmo landslide K. Jin et al.
56. Characterizing coarse sediment grain size variability along the upper Sandy River, Oregon, via UAV remote sensing E. Levenson & M. Fonstad