The field of unmanned aerial vehicle (UAV) technology is rapidly advancing and has become increasingly popular in various fields, including surveying and mapping. The ability of UAVs to capture high-resolution images of the Earth's surface with ease and precision has made them an invaluable tool for collecting data in remote and hard-to-reach areas. However, the accuracy of the data obtained through UAVs is a critical factor that must be carefully evaluated to ensure the reliability and effectiveness of the data collected. The paper focuses on exploring different methodologies for determining the accuracy of UAV data processing and approaches for preliminary assessment based on Ground Sampling Distance (GSD). The DJI Phantom 4 Professional UAV was used for data collection, and the Agisoft PhotoScan Professional software was employed for processing. To evaluate the accuracy, the study calculated deviations of control points, and the method was applied to a flat terrain area and a ravine area. Additionally, the method of determining deformation of linear contours and areas was tested for the construction site of a technical maintenance station during the installation of column foundations. The experiments revealed that the maximum error value ranged from 1 to 3 GSD, indicating the feasibility of performing a preliminary accuracy assessment of low-cost UAV data based on the camera's resolution parameter. The study's findings have significant implications for the field of UAV technology, as they can help pave the way for the development of more efficient and reliable data collection techniques. Moreover, the research can provide valuable insight into improving the accuracy of UAV data processing, which can have a considerable impact on fields such as surveying, mapping, and remote sensing. By expanding our understanding of the accuracy of UAV data, we can unlock new opportunities for scientific research and exploration, leading to advances in various fields.
Snavely, N., Seitz, S.M. & Szeliski, R. Modeling the World from Internet Photo Collections. Int J Comput Vis 80, 189–210 (2008). https://doi.org/10.1007/s11263-007-0107-3 [in English]
Cook, K. L. (2017). An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection. Geomorphology, 278, 195–208. https://doi.org/10.1016/j.geomorph.2016.11.009 [in English]
Duró, G., Crosato, A., Kleinhans, M. G., & Uijttewaal, W. S. J. (2018). A low-cost technique to measure bank erosion proces ses along middle-size river reaches. Earth Surface Dynamics Discussions, March, 1–28. https://doi.org/10.5194/esurf-2018-3 [in English]
Gindraux, S., Boesch, R., & Farinotti, D. (2017). Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers. Remote Sensing, 9(2), 186. https://doi.org/10.3390/rs9020186 [in English]
Nagendran, S. K., Tung, W. Y., & Mohamad Ismail, M. A. (2018). Accuracy assessment on low altitude UAV-borne photogrammetry outputs influenced by ground control point at different altitude. IOP Conference Series: Earth and Environmental Science, 169(1). https://doi.org/10.1088/1755-1315/169/1/012031 [in English]
Siedov, А. О. (2018). Possibilities of Use of the UAVs of the Average Price Segment for Mapping of Agricultural Resources. Visnyk of V. N. Karazin Kharkiv National University series «Ecology», 517(18), 22–29. https://doi.org/10.26565/1992-4259-2018-18-03 [in Ukrainian]
Kolb, I., Vivat, A., Nazarchuk, N., Zhyvchuk, V., & Pashchetnyk, O. (2022). Methodology of 3D Modeling Based on Aerial Images from a Drone with Simplified Geodetic Reference. 16th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, November 2022, 1–5. https://doi.org/10.3997/2214-4609.2022580240 [in English]
Luhmann, T., Chizhova, M., Gorkovchuk, D., Hastedt, H., Chachava, N., & Lekveishvili, N. (2019). Combination of terrestrial laserscanning, uav and close-range photogrammetry for 3D reconstruction of complex churches in Georgia. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2/W11), 753–761. https://doi.org/10.5194/isprs-Archives-XLII-2-W11-753-2019 [in English]
Schumann, Muhlhausen, & Andreadis. (2019). Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory. Remote Sensing, 11(8), 982. https://doi.org/10.3390/rs11080982 [in English]
Polat, N., & Uysal, M. (2018). An Experimental Analysis of Digital Elevation Models Generated with Lidar Data and UAV Photogrammetry. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-018-0760-8 [in English]