13 citations as recorded by crossref.

1. 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
2. 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
3. Stage–discharge relationship in an erodible compound channel with overbank floods H. Fu et al. 10.1016/j.jhydrol.2024.131181
4. The influence of coarse particle abundance and spatial distribution on sediment transport and cluster evolution in steep channels under sediment-starved conditions W. Li et al. 10.1016/j.catena.2023.107199
5. 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
6. POSSIBILITIES OF USING FIJIIMAGEJ2, WIPFRAG AND BASEGRAIN PROGRAMS FOR MORPHOMETRIC AND GRANULOMETRIC SOIL ANALYSIS Y. Tsytsiura 10.1590/1809-4430-eng.agric.v43n6e20230101/2023
7. Improved Faster R-CNN for the Detection Method of Industrial Control Logic Graph Recognition S. Wu et al. 10.3389/fbioe.2022.944944
8. Influence of Sediment Supply Timing on Bedload Transport and Bed Surface Texture During a Single Experimental Hydrograph in Gravel Bed Rivers M. Hassan et al. 10.1029/2023WR035406
9. 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
10. 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
11. Classification of Lakebed Geologic Substrate in Autonomously Collected Benthic Imagery Using Machine Learning J. Geisz et al. 10.3390/rs16071264
12. 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
13. 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