In this study a comprehensive analysis of the efficiency of implementing LiDAR technology (Light Detection and Ranging) in the formation of high-precision digital terrain models (DTMs) in the course of large-scale topogeodetic surveys is carried out. The aim of the research is to evaluate LiDAR accuracy indicators, economic feasibility and operational performance relative to classical photogrammetry, taking into account the use of unmanned aerial vehicles (UAVs). The methodological basis of the research includes a review of publications, synthesis of data from these works and statistical data analysis. The results obtained indicate that LiDAR provides an advantage in digitizing terrain under dense vegetation cover. An algorithm for selecting the optimal method is proposed, based on multi-criteria analysis which includes vegetation density, accuracy requirements and project budget constraints. The key findings of the study emphasize the superiority of LiDAR in complex natural-landscape conditions and the economic viability of photogrammetry in areas with open terrain, which justifies the feasibility of a hybrid approach to optimize costs and improve the quality of the output DTMs. The study will be useful for surveying engineers, GIS specialists, managers of construction and infrastructure projects, as well as researchers in the field of Earth remote sensing.

LiDAR, digital terrain model (DTM), UAV, photogrammetry

Geospatial Solutions Market by Solution Solution (Hardware, Software, Service), End-User (Utility, Business, Transportation, Defense & Intelligence, Infrastructural Development), Application, Region – Global Forecast to 2024. Retrieved from: https://www.marketsandmarkets.com/Market-Reports/geospatial-solution-market-206125202.html (date of access: 17.06.2025).

Pinton, D., et al. (2023). Estimating ground elevation and vegetation characteristics in coastal salt marshes using UAV-based LiDAR and digital aerial photogrammetry. Remote Sensing, 15(1), 1–24. https://doi.org/10.3390/rs15010226

Zhou, L., et al. (2022). Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment. Urban Forestry & Urban Greening, 69. https://doi.org/10.1016/j.ufug.2022.127489

Jarahizadeh, S., & Salehi, B. (2024). A comparative analysis of UAV photogrammetric software performance for forest 3D modeling: A case study using AgiSoft photoscan, PIX4DMapper, and DJI Terra. Sensors, 24(1), 1–15. https://doi.org/10.3390/s24010286

Silva-Fragoso, A., et al. (2024). Improving the Accuracy of Digital Terrain Models Using Drone-Based LiDAR for the Morpho-Structural Analysis of Active Calderas: The Case of Ischia Island, Italy . Remote Sensing, 16(11), 1–33. https://doi.org/10.3390/rs16111899

Sestras, P., et al. (2025). Land surveying with UAV photogrammetry and LiDAR for optimal building planning . Automation in Construction,173. https://doi.org/10.1016/j.autcon.2025.106092

Sterpin, A., & Medici, M. (2023). Low-Cost UAV Photogrammetry and GNSS Technology for Digital Terrain Modeling: The DIACHRONIC LANDSCAPES Workshop Case, 1-33.

Shi, S., et al. (2021). Land cover classification with multispectral LiDAR based on multi-scale spatial and spectral feature selection . Remote Sensing, 13(20), 1–21. https://doi.org/10.3390/rs13204118

Grand View Research. (n.d.). LiDAR market size, share & trends analysis report by type (airborne, terrestrial), by application (engineering, environment), by component (GPS, navigation), by region, and segment forecasts, 2025–2030. Retrieved from https://www.grandviewresearch.com/industry-analysis/lidar-light-detection-and-ranging-market (date of access: 18.06.2025).

Galanakis, D., et al. (2023). SVD-based point cloud 3D stone by stone segmentation for cultural heritage structural analysis–The case of the Apollo Temple at Delphi. Journal of Cultural Heritage, 61, 177–187. https://doi.org/10.1016/j.culher.2023.04.005

Li, B., et al. (2022). Terrain-Net: A highly-efficient, parameter-free, and easy-to-use deep neural network for ground filtering of UAV LiDAR data in forested environments. Remote Sensing, 14(22), 1–21. https://doi.org/10.3390/rs14225798

Winsen, M., & Hamilton, G. (2023). A comparison of UAV-derived dense point clouds using LiDAR and NIR photogrammetry in an Australian eucalypt forest. Remote Sensing, 15(6), 1–24. https://doi.org/10.3390/rs15061694

Kanostrevac D. et al. (2019). Data quality comparative analysis of photogrammetric and Lidar DEM. Micro Macro Mezzo Geo Inf, 12, 17-34.