AUTOMATIC MUCK PILE CHARACTERIZATION FROM UAV IMAGES

12 citations as recorded by crossref.

1. Bergbaukunde, quo vadis? M. Tost et al. https://doi.org/10.1007/s00501-023-01322-x
2. Review of deep learning approaches in solving rock fragmentation problems M. Ronkin et al. https://doi.org/10.3934/math.20231219
3. Continuous Monitoring and Improvement of the Blasting Process in Open Pit Mines Using Unmanned Aerial Vehicle Techniques T. Bamford et al. https://doi.org/10.3390/rs12172801
4. High-fidelity DEMs for complex open-pit mines via multi-scale morphology and adaptive modelling S. Zhen et al. https://doi.org/10.1088/1361-6501/ae7c96
5. UAV in the advent of the twenties: Where we stand and what is next F. Nex et al. https://doi.org/10.1016/j.isprsjprs.2021.12.006
6. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex https://doi.org/10.3390/drones3030074
7. Deep Learning-Based Rock Muck Detection on Point Clouds During TBM Excavation P. Jiang et al. https://doi.org/10.1109/JSEN.2024.3421671
8. Convex–concave feature-based fragmentation calculation for blast muck piles in open-pit copper mines from 3D laser point cloud data Y. Wang et al. https://doi.org/10.1080/10095020.2026.2638723
9. Image analytics enabled real-time decision making in iron ore processing at Tata Steel India C. Das et al. https://doi.org/10.1007/s12597-026-01290-z
10. A novel intelligent stereo vision approach for blast-induced fragmentation size distribution: Case study at Golgohar open-pit mine, Iran M. Bahraini & I. Atighi https://doi.org/10.1016/j.mineng.2024.108822
11. Rock Fragmentation and Particle Size Distribution Analysis: A Comparative Study of Image-Based Photogrammetry and LiDAR Point Cloud Analysis M. Faramarzi H. et al. https://doi.org/10.1007/s42461-026-01625-w
12. Online particle size analysis on conveyor belts with dense convolutional neural networks Y. Fu & C. Aldrich https://doi.org/10.1016/j.mineng.2023.108019