Optimizing ground control points for UAV photogrammetry: A case study in slope stability mapping
Main Article Content
Abstract
This study investigated the effect of Ground Control Point (GCP) distribution on the accuracy of UAV-based slope mapping and stability analysis. Three GCP configurations—top-only, vertical, and diagonal—were tested. Accuracy was evaluated using UAV photogrammetry and compared to GPS geodetic data. The vertical GCP setup produced the highest accuracy, reducing total RMSE by 89.6% (from 52.93 mm to 5.50 mm). The diagonal configuration, while being slightly less accurate (61.26 mm RMSE), improved spatial coverage. Slope stability analysis using the finite element method (FEM) confirmed the reliability of the vertical setup for slope assessment. These results demonstrated that optimizing GCP layout could significantly improve model precision while reducing fieldwork. This work contributes to efficient and accurate slope monitoring with fewer GCPs, making it suitable for large-scale geotechnical applications. Future research will focus on applying these configurations to vegetated and more complex terrains and integrating automation for broader and scalable implementation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
F. Nex, C. Armenakis, M. Cramer, D.A. Cucci, M. Gerke, E. Honkavaara et al., UAV in the advent of the twenties: Where we stand and what is next, ISPRS J. Photogramm. Remote Sens. 184 (2022) 215–242.
S. Wang, W. Zhang, X. Zhao, Q. Sun and W. Dong, Automatic identification and interpretation of discontinuities of rock slope from a 3D point cloud based on UAV nap-of-the-object photogrammetry, Int. J. Rock Mech. Min. Sci. 178 (2024) 105774.
F. Nobahar, M.; Salunke, R.; Alzeghoul, O. E.; Khan, M. S.; Amini, Mapping of Slope Failures on Highway Embankments Using Electrical Resistivity Imaging (ERI), Unmanned Aerial Vehicle (UAV), and Finite Element Method (FEM) Numerical Modeling for Forensic Analysis, Transp. Geotech. 40 (2023) 100949.
K. Tempa, K. Peljor, S. Wangdi, R. Ghalley, K. Jamtsho, S. Ghalley et al., UAV technique to localize landslide susceptibility and mitigation proposal: A case of Rinchending Goenpa landslide in Bhutan, Nat. Hazards Res. 1 (2021) 171–186.
F. Aguera-Vega, F. Carvajal-Ramírez and P. Martínez-Carricondo, Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle, Meas. J. Int. Meas. Confed. 98 (2017) 221–227.
P. Martínez-Carricondo, F. Agüera-Vega, F. Carvajal-Ramírez, F.J. Mesas-Carrascosa, A. García-Ferrer and F.J. Pérez-Porras, Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points, Int. J. Appl. Earth Obs. Geoinf. 72 (2018) 1–10.
P. Olaszek, I. Wycza?ek, D. Sala, M. Kokot and A. ?wiercz, Monitoring of the static and dynamic displacements of railway bridges with the use of inertial sensors, Sensors (Switzerland) 20 (2020) .
A.F. Silva, J.M.G. Sotomayor and V.F.N. Torres, Correlations of geotechnical monitoring data in open pit slope back-analysis - A mine case study, J. South. African Inst. Min. Metall. 121 (2021) 557–564.
Y. Dwikarsa and A. Basith, Benthic habitats classification using multi scale parameters of GEOBIA on orthophoto images of Karimunjawa waters, Commun. Sci. Technol. 6 (2021) 55–59.
X. Ren, M. Sun, C. Jiang, L. Liu and W. Huang, An augmented reality geo-registration method for ground target localization from a low-cost UAV platform, Sensors (Switzerland) 18 (2018) .
Y. Erzin and N. Ecemis, The use of neural networks for the prediction of cone penetration resistance of silty sands, Neural Comput. Appl. 28 (2017) 727–736.
O. de Freitas Neto, O. Santos, F. Franca and R. Severo, Influence of Compaction Energy and Bentonite Clay Content in the Soil Hydraulic Conductivity, Appl. Mech. Mater. 851 (2016) 858–863.
I. Elkhrachy, Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry, Alexandria Eng. J. 60 (2021) 5579–5590.
R. Boulanger and I. Idriss, CPT-Based Liquefaction Triggering Procedure, J. Geotech. Geoenvironmental Eng. 142 (2015) 4015065.
D.M. Seo, H.J. Woo, W.H. Hong, H. Seo and W.J. Na, Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production, Appl. Sci. 14 (2024) .
S. Gindraux, R. Boesch and D. Farinotti, Accuracy assessment of digital surface models from Unmanned Aerial Vehicles’ imagery on glaciers, Remote Sens. 9 (2017) 1–15.
F. Carvajal-Ramírez, F. Agüera-Vega and P.J. Martínez-Carricondo, Effects of image orientation and ground control points distribution on unmanned aerial vehicle photogrammetry projects on a road cut slope, J. Appl. Remote Sens. 10 (2016) 034004.
F. Niazi, CPT-Based Geotechnical Design Manual, Volume 1: CPT Interpretation—Estimation of Soil Properties. (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2021/22), I (2021) .
H. Zhou, L.M. Wotherspoon, A.C. Stolte and M. Holtrigter, Assessment and development of empirical correlations between flat plate dilatometer and cone penetration tests for Auckland soils, New Zeal. J. Geol. Geophys. (ahead print) (2024) 1–19.
T. Tamosiunas, G. Zarzojus and S. Skuodis, Indirect Determination of Soil Young’S Modulus in Lithuania Using Cone Penetration Test Data, Balt. J. Road Bridg. Eng. 17 (2022) 1–24.
I. Papa, M. Popovic, L. Hodak, A. Duka, T. Pentek, M. Hikl et al., Water and Vegetation as a Source of UAV Forest Road Cross-Section Survey Error, Forests 16 (2025) 1–18.
J. Rotnicka, M. Dluzewski, M. Dabski, M. Rodzewicz, W. Wlodarski and A. Zmarz, Accuracy of the UAV-Based DEM of Beach–Foredune Topography in Relation to Selected Morphometric Variables, Land Cover, and Multitemporal Sediment Budget, Estuaries and Coasts 43 (2020) 1939–1955.
T. Kloucek, P. Klápšte, J. Marešová and J. Komárek, UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation, Remote Sens. 14 (2022) .
X. Cui, X. Zhang, Z. Xu and H. Liu, The application of dr one tilt photog r aphy in la r ge s cale topog r aphic mapping, in Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 12978 (2024), pp. 3020998.
M. Frid, V. Frid and G. Scholar, Advancements in Drone-based (UAV) Ground Penetrating Radar for Accurate Boundary Mapping between Disturbed Clayey Soil and Natural Rock, Preprints (2024) 1–12.
C.S. Nijjar, S. Singh, T. Jaiswal and S. Kalra, High-Resolution Mapping of Forest Canopy Cover Using UAV and Sentinel-2, in Proceedings of UASG 2021: Wings 4 Sustainability. UASG 2021. Lecture Notes in Civil Engineering, K. Jain, V. Mishra and B. Pradhan, eds., Springer, Cham, 2023, pp. 331–341.
J. Na, K. Xue, L. Xiong, G. Tang, H. Ding, J. Strobl et al., UAV-based terrain modeling under vegetation in the chinese loess plateau: A deep learning and terrain correction ensemble framework, Remote Sens. 12 (2020) 1–18.
T.N. Nguyen, T.B. Nguyen, T. Van Chien and T.H. Nguyen, Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring, Int. J. Electr. Electron. Eng. Telecommun. 12 (2023) 317–325.