Abstract
Identifying discontinuities in high, steep rock slopes is challenging. This study proposes a high-precision geometric feature measurement method for discontinuities on the basis of point cloud data acquired via unmanned aerial vehicle (UAV) photography. The method effectively extracts key parameters, including orientation, trace length, and spacing. The implementation process comprises five main steps. First, principal component analysis (PCA) is used to extract feature information from the point cloud data. Second, the point cloud is preliminarily segmented via a curvature threshold and the density-based spatial clustering with noise (DBSCAN) algorithm. Third, the density peak clustering (DPC) algorithm is adopted to identify cluster centers and divide the discontinuity sets. Fourth, secondary DBSCAN clustering is performed on each discontinuity set to obtain complete individual discontinuities. Finally, geometric characteristics such as orientation, trace length, and spacing are measured on the basis of the principles of analytic geometry. The experimental results show that the orientation deviation calculated by this method is within an acceptable range and that the proposed method has higher computational efficiency than the traditional DPC method. The influence of random fracture networks (DFNs) on the stability of rock slopes was investigated via discrete element numerical simulations.
Similar content being viewed by others
Data availability
The data and materials that support the findings of this study are available from the author Xinlong Liu upon reasonable request.
References
Ma, X. et al. Identification and characterization of rock discontinuities under complex terrain conditions based on UAV photogrammetry and ANN algorithm. Bull. Eng. Geol. Environ. 84, 170. https://doi.org/10.1007/s10064-025-04193-3 (2025).
Migliazza, M., Carriero, M. T., Lingua, A., Pontoglio, E. & Scavia, C. Rock mass characterization by UAV and close-range photogrammetry: A multiscale approach applied along the Vallone dell’Elva Road. Geosciences 11, 436. https://doi.org/10.3390/geosciences11110436 (2021).
Cai, X., Lü, Q., Zheng, J., Liao, K.-W. & Liu, J. An efficient adaptive approach to automatically identify rock discontinuity parameters using 3D point cloud model from outcrops. Geol. J. 58(6), 2195–2210. https://doi.org/10.1002/gj.4708 (2023).
Li, T. et al. Exposed linear discontinuity recognition on a high steep slope using UAV multisource image fusion. Rock Mech. Rock Eng. 58, 9669–9693. https://doi.org/10.1007/s00603-025-04652-z (2025).
Tang, M. et al. Automatic extraction of rock discontinuities from the point cloud using dynamic DBSCAN algorithm. Adv. Civ. Eng. 2022, 7754179. https://doi.org/10.1155/2022/7754179 (2022).
Khanna, R. & Dubey, R. K. Comparative assessment of slope stability along road-cuts through rock slope classification systems in Kullu Himalayas. Bull. Eng. Geol. Environ. 80, 993–1017. https://doi.org/10.1007/s10064-020-02021-4 (2021).
Muhammad, J., Rini, A. A., Radzuan, S. & Mohd, N. A. A. An expeditious approach for slope stability assessment using integrated 2D electrical resistivity tomography and unmanned aerial vehicle survey. J. Appl. Geophys. 205, 104778. https://doi.org/10.1016/j.jappgeo.2022.104778 (2022).
Loupasakis, C. et al. Investigating the stability of the Hill of the Acropolis of Athens, Greece, using fuzzy logic and remote sensing techniques. Remote Sens. 15, 1067. https://doi.org/10.3390/rs15041067 (2023).
Abellán, A. et al. Terrestrial laser scanning of rock slope instabilities. Earth Surf. Process. Landforms 39, 80–97. https://doi.org/10.1002/esp.3493 (2014).
Haneberg, W. C. Using close range terrestrial digital photogrammetry for 3-D rock slope modeling and discontinuity mapping in the United States. Bull. Eng. Geol. Environ. 67, 457–469. https://doi.org/10.1007/s10064-008-0157-y (2008).
Li, M. et al. Characterization and stability analysis of rock mass discontinuities in layered slopes: A case study from Fushun West Open-Pit Mine. Appl. Sci. 14, 11330. https://doi.org/10.3390/app142311330 (2024).
Hartwig, M. E. & Santos, GGd. Sd. Enhanced discontinuity mapping of rock slopes exhibiting distinct structural frameworks using digital photogrammetry and UAV imagery. Environ. Earth Sci. 83, 624. https://doi.org/10.1007/s12665-024-11939-x (2024).
Chen, N., Hao, Y., Wang, C. & Zheng, J. Semiautomatic identification of discontinuity parameters in rock masses based on unmanned aerial vehicle photography. Geol. J. 59(9), 2401–2415. https://doi.org/10.1002/gj.4905 (2024).
Li, H., Li, X., Li, W., Zhang, S. & Zhou, J. Quantitative assessment for the rockfall hazard in a postearthquake high rock slope using terrestrial laser scanning. Eng. Geol. 248, 1–13. https://doi.org/10.1016/j.enggeo.2018.11.003 (2019).
Wu, X. et al. A new method for automatic extraction and analysis of discontinuities based on TIN on rock mass surfaces. Remote Sens. 13, 2894. https://doi.org/10.3390/rs13152894 (2021).
Liu, Y., Hua, W., Chen, Q. & Liu, X. Characterization of complex rock mass discontinuities from LiDAR point clouds. Remote Sens. 16, 3291. https://doi.org/10.3390/rs16173291 (2024).
Chen, N. et al. Semiautomatic identification of tunnel discontinuity based on 3D laser scanning. Geotech. Geol. Eng. 42, 2577–2599. https://doi.org/10.1007/s10706-023-02692-2 (2024).
Lu, G. et al. Identification of rock mass discontinuity from 3D point clouds using improved fuzzy C-means and convolutional neural network. Bull. Eng. Geol. Environ. 83(5), 159 (2024).
Cao, B. et al. Semiautomatic measurement for rock mass discontinuity orientation, trace and spacing from point clouds. Measurement 246, 116688 (2025).
Yalcin, I., Can, R., Gokceoglu, C. & Kocaman, S. A novel rock mass discontinuity detection approach with CNNs and multi-view image augmentation. ISPRS Int. J. Geo-Inf. 13(6), 185. https://doi.org/10.3390/ijgi13060185 (2024).
Palazzo, S. et al. Domain adaptation for outdoor robot traversability estimation from RGB data with safety-preserving loss. Int. Conf. Intell. Robots Syst. https://doi.org/10.1109/IROS45743.2020.9341044 (2020).
Kang, J. et al. Semiautomatic identification of rock discontinuity orientation based on 3D point clouds and its engineering application. Bull. Eng. Geol. Environ. 83, 172. https://doi.org/10.1007/s10064-024-03681-2 (2024).
Ma, L. et al. A multilevel classification strategy for the identification of discontinuities from 3D point clouds of complicated rock surfaces. Rock Mech. Rock Eng. 57, 10611–10630. https://doi.org/10.1007/s00603-024-04109-9 (2024).
Kong, D., Wu, F. & Saroglou, C. Automatic identification and characterization of discontinuities in rock masses from 3D point clouds. Eng. Geol. 265, 105442. https://doi.org/10.1016/j.enggeo.2019.105442 (2020).
Zhu, J., Xia, Y., Wang, B., Yang, Z. & Yang, K. Research on the identification of rock mass structural planes and extraction of dominant orientations based on 3D point cloud. Appl. Sci. 14, 9985. https://doi.org/10.3390/app14219985 (2024).
Ozturk, H. S., Kocaman, S. & Gokceoglu, C. A low-cost approach for determination of discontinuity orientation using smartphone images and application to a part of Ihlara Valley (Central Turkey). Eng. Geol. https://doi.org/10.1016/j.enggeo.2019.04.011 (2019).
Kang, J. et al. Semiautomatic identification of rock discontinuity orientation based on 3D point clouds and its engineering application. Bull. Eng. Geol. Environ. 83(5), 1–18 (2024).
Zang, W. et al. DPC-MFP: An adaptive density peaks clustering algorithm with multiple feature points. Neurocomputing 618, 129060. https://doi.org/10.1016/j.neucom.2024.129060 (2025).
Cao, B. et al. Semiautomatic measurement for rock mass discontinuity orientation, trace and spacing from point clouds. Measurement 246, 116688. https://doi.org/10.1016/j.measurement.2025.116688 (2025).
Gu, D. & Huang, D. A complex rock topple-rock slide failure of an anaclinal rock slope in the Wu Gorge, Yangtze River, China. Eng. Geol. 208, 165–180. https://doi.org/10.1016/j.enggeo.2016.04.037 (2016).
Funding
This research was funded by the Deep Underground National Science and Technology Major Project of China (Grant No. 2024ZD1001400).
Author information
Authors and Affiliations
Contributions
Conceptualization, L.W.; methodology, X.L.; software, X.L.; validation, Y.W. and B.W.; formal analysis, Y.W.; investigation, X.L. and Z.L.; resources, L.W.; data curation, X.L. and Z.L.; writing—original draft preparation, S.W.; writing—review and editing, X.L. and Z.L.; visualization, J.Y.; supervision, J.Y.; project administration, X.Y.; funding acquisition, L,W. All the authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Wu, L., Wang, Y., Yang, J. et al. Application of UAV photogrammetry technology in identifying discontinuities in slopes in the Pulang copper mine. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43520-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-43520-w
