 |
[email protected] |
 |
3275638434 |
 |
 |
| Paper Publishing WeChat |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Effect of Image Resolution on UAV-Based Photogrammetric Accuracy for Civil Engineering Applications
Mostafa Abdel-Bary Ebrahim
Full-Text PDF
XML 89 Views
DOI:10.17265/1934-7359/2025.07.002
Civil and Environmental Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Kingdom of Saudi Arabia
This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.
UAV photogrammetry, image resolution, 3D measurements, civil engineering, Saudi Vision 2030.
Journal of Civil Engineering and Architecture 19 (2025) 317-326
doi: 10.17265/1934-7359/2025.07.002
[1] Luhmann, T., Robson, S., Kyle, S., and Boehm, J. 2020. Close-Range Photogrammetry and 3D Imaging (3rd ed.). Boston: De Gruyter. https://www.degruyterbrill.com/document/doi/10.1515/9783110607253/html.
[2] Tmusic, G., Manfreda, S., Aasen, H., James, M. R., Gonçalves, G., Ben-Dor, E., Brook, A., Polinova, M., Arranz, J. J., Mészáros, J., Zhuang, R., Johansen, K., Malbeteau, Y., de Lima, I. P., Davids, C., Herban, S., and McCabe, M. F. 2020. “Current Practices in UAS-Based Environmental Monitoring.” Remote Sensing 12 (6): 1001. https://doi.org/10.3390/rs12061001.
[3] Remondino, F., and El-Hakim, S. 2020. “Image-Based 3D Modelling: A Review.” The Photogrammetric Record 35 (171): 269-91. https://doi.org/10.1111/phor.12345.
[4] Nocerino, E., Menna, F., Gruen, A., Troyer, M., Capra, A., Castagnetti, C., Rossi, P., Brooks, A. J., Schmitt, R. J., and Holbrook, S. J. 2020. “Coral Reef Monitoring by Scuba Divers Using Underwater Photogrammetry and Geodetic Surveying.” Remote Sensing 12 (18): 3036. https://doi.org/10.3390/rs12183036.
[5] Fraser, C. S. 2015. “Advances in Close-Range Photogrammetry.” Photogrammetric Week 15: 257-68. https://phowo.ifp.uni-stuttgart.de/publications/phowo15/260Fraser.pdf.
[6] Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., and Johnson, B. A. 2019. “Deep Learning in Remote Sensing Applications: A Meta-analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152: 166-77. https://doi.org/10.1016/j.isprsjprs.2019.04.015.
[7] Wang, Q., Tan, Y., and Mei, Z. 2020. “Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications.” Archives of Computational Methods in Engineering 27 (5): 1667-717. https://doi.org/10.1007/s11831-019-09320-4.
[8] Pavelka, K., Raeva, P., and Soha, M. 2021. “New Measurement Methods for Structure Deformation.” IOP Conference Series: Earth and Environmental Science 906: 012060. https://doi.org/10.1088/1755-1315/906/1/012060.
[9] Hoek Spaans, R., Drumond, B., van Daalen, K. R., Rorato Vitor, A. C., Derbyshire, A., Da Silva, A., Lana, R. M., Vega, M. S., Carrasco-Escobar, G., Sobral Escada, M. I., Codeço, C., and Lowe, R. 2024. “Ethical Considerations Related to Drone Use for Environment and Health Research: A Scoping Review Protocol.” PLoS ONE 19 (1): e0287270. https://doi.org/10.1371/journal.pone.0287270.
[10] Eltner, A., and Schneider, D. 2015. “Analysis of Different Methods for 3D Reconstruction of Natural Surfaces from Parallel-Axes UAV Images.” The Photogrammetric Record 30 (151): 279-99. https://doi.org/10.1111/phor.121 15.
[11] Aela, P., Chi, H.-L., Fares, A., Zayed, T., and Kim, M. 2024. “UAV-Based Studies in Railway Infrastructure Monitoring.” Automation in Construction 167: 105714. https://doi.org/10.1016/j.autcon.2024.105714.
[12] Finn, R., and Wright, D. 2020. “Privacy and Data Protection in UAV Operations.” Computer Law & Security Review 37: 105398. https://doi.org/10.1016/j.clsr.2020.105398.
[13] Giordan, D., Adams, M. S., Aicardi, I., Alicandro, M., Allasia, P., Baldo, M., De Berardinis, P., Dominici, D., Godone, D., Hobbs, P. R., Lechner, V., Niedzielski, T., Piras, M., Rotilio, M., Salvini, R., Segor, V., Sotier, B., and Troilo, F. 2020. “The Use of Unmanned Aerial Vehicles (UAVs) for Engineering Geology Applications.” Bulletin of Engineering Geology and the Environment 79 (7): 3437-81. https://doi.org/10.1007/s10064-020-01766-1.
[14] Montgomery, D. C. 2020. Design and Analysis of Experiments (10th ed.). New York: Wiley. https://www.wiley.com/en-us/Design+and+Analysis+of+Experiments%2C+10th+Edition-p-9781119492443.
[15] Dano, U. L., Abubakar, I. R., AlShihri, F. S., Ahmed, S. M. S., Alrawaf, T. I., and Alshammari, M. S. 2023. “A Multi-criteria Assessment of Climate Change Impacts on Urban Sustainability in Dammam Metropolitan Area, Saudi Arabia.” Ain Shams Engineering Journal 14 (9): 102062. https://doi.org/10.1016/j.asej.2022.102062.
[16] Kerle, N., Nex, F., Gerke, M., Duarte, D., and Vetrivel, A. 2020. “UAV-Based Structural Damage Mapping: A Review.” ISPRS International Journal of Geo-Information 9 (1): 14. https://doi.org/10.3390/ijgi9010014.