34 citations as recorded by crossref.

1. Study on the automated characterization of particle size and shape of stacked gravelly soils via deep learning J. Gong et al. https://doi.org/10.1007/s11440-024-02493-8
2. Stage–discharge relationship in an erodible compound channel with overbank floods H. Fu et al. https://doi.org/10.1016/j.jhydrol.2024.131181
3. 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. https://doi.org/10.1016/j.catena.2023.107199
4. 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
5. Improved Faster R-CNN for the Detection Method of Industrial Control Logic Graph Recognition S. Wu et al. https://doi.org/10.3389/fbioe.2022.944944
6. 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
7. 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
8. 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
9. Deep Learning and Histogram-Based Grain Size Analysis of Images W. Wei et al. https://doi.org/10.3390/s24154923
10. 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
11. Gravel automatic sieving method fusing macroscopic and microscopic characteristics S. Gao et al. https://doi.org/10.1016/j.ijsrc.2024.05.002
12. Quantification of particle size and shape of sands based on the combination of GAN and CNN J. Gong et al. https://doi.org/10.1016/j.powtec.2024.120122
13. 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. https://doi.org/10.1029/2023WR035406
14. A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width W. Li et al. https://doi.org/10.3390/rs16234614
15. Sediment shape as a proxy for fluvial processes: A factorial analysis in the Yarlung Tsangpo Grand Canyon K. Li et al. https://doi.org/10.1111/sed.70108
16. Back-analysis of the 2000 Yigong dam breach flood morphodynamics: Challenges and promises Y. Lei et al. https://doi.org/10.1016/j.geomorph.2024.109588
17. 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
18. Quantifying particle morphology via lightweight object detection and generative data augmentation Z. Liu et al. https://doi.org/10.1007/s11440-026-02949-z
19. POSSIBILITIES OF USING FIJIIMAGEJ2, WIPFRAG AND BASEGRAIN PROGRAMS FOR MORPHOMETRIC AND GRANULOMETRIC SOIL ANALYSIS Y. Tsytsiura https://doi.org/10.1590/1809-4430-eng.agric.v43n6e20230101/2023
20. La mesure granulométrique par imagerie en contexte torrentiel : apports et perspectives des nouvelles méthodes par intelligence artificielle J. Berthet et al. https://doi.org/10.4000/15pwo
21. ImageGrains 2.0: Improved precision and generalization for grain segmentation D. Mair et al. https://doi.org/10.5194/esurf-14-527-2026
22. 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
23. PlanetGSD 1.0: a cross-planetary grain-size distribution dataset from the Earth, the Moon, and the Mars J. Zhang & Y. Li https://doi.org/10.5194/essd-18-4983-2026
24. 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
25. 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
26. Classification of Lakebed Geologic Substrate in Autonomously Collected Benthic Imagery Using Machine Learning J. Geisz et al. https://doi.org/10.3390/rs16071264
27. Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al. https://doi.org/10.1002/esp.5984
28. Debris flow susceptibility mapping using neural networks and deep learning with SHAP-based interpretation in Chaharmahal and Bakhtiari Province, a semi-arid mountainous region of Iran S. Yousefi et al. https://doi.org/10.1007/s12145-026-02179-7
29. 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
30. Artificial Intelligence for Mineral Exploration Imagery: A Systematic Mapping Review of Data, Tasks, and Methods Y. Xiao et al. https://doi.org/10.3390/min16100984
31. Application of deep learning in magnetic spherule detection: a combined method of YOLOv8 and U-Net models Z. Cui & Y. Wang https://doi.org/10.1007/s12145-025-01901-1
32. Characterizing segregation in blast rock piles: A deep-learning approach leveraging aerial image analysis C. Liu et al. https://doi.org/10.1016/j.powtec.2025.121244
33. Curvature-based pebble segmentation for reconstructed surface meshes A. Rheinwalt et al. https://doi.org/10.5194/esurf-13-923-2025
34. Field characteristics and initiation mechanisms of debris floods: Insights from western Beijing, China B. Zhang et al. https://doi.org/10.1016/j.geomorph.2026.110584