13 citations as recorded by crossref.

1. 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
2. Remotely sensed rivers in the Anthropocene: state of the art and prospects H. Piégay et al. 10.1002/esp.4787
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 10.1016/j.rse.2020.111799
4. 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
5. 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
6. A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches A. Soloy et al. 10.3390/rs12213659
7. Measuring the grain‐size distributions of mass movement deposits E. Harvey et al. 10.1002/esp.5337
8. Adopting deep learning methods for airborne RGB fluvial scene classification P. Carbonneau et al. 10.1016/j.rse.2020.112107
9. Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes B. Purinton & B. Bookhagen 10.1029/2021JF006260
10. Revisiting the automated grain sizing technique (AGS) for characterizing grain size distribution M. Sulaiman et al. 10.1080/15715124.2021.1917585
11. 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
12. FKgrain: A topography-based software tool for grain segmentation and sizing using factorial kriging F. Wu et al. 10.1007/s12145-021-00660-z
13. The gravel-sand transition and grain size gap in river bed sediments E. Dingle et al. 10.1016/j.earscirev.2021.103838