Comparison of UAV and Terrestrial Laser Scanning for Urban 3D Mapping

Thesis Abstract

Rapid urbanization necessitates precise and efficient three-dimensional (3D) mapping techniques to support urban planning, infrastructure development, and environmental management. Despite the proliferation of remote sensing technologies, there remains a knowledge gap regarding the comparative effectiveness, accuracy, and operational practicality of Unmanned Aerial Vehicles (UAVs) equipped with photogrammetric sensors versus Terrestrial Laser Scanning (TLS) systems in complex urban environments. This study aims to critically evaluate and compare these two prominent 3D data acquisition methods to determine their suitability, accuracy, and operational efficiencies for urban mapping applications. The specific objectives are to (1) assess the spatial accuracy of UAV-based photogrammetric point clouds relative to TLS data, (2) analyze the operational efficiency and data processing workflows of both techniques, and (3) identify the strengths and limitations of each method within diverse urban contexts characterized by mixed building heights, narrow streets, and dense infrastructure. The research adopts a comparative case study design within a metropolitan area urban setting, focusing on three representative zones varying in built-up density. A total sample of twenty sites, equally divided among high-density urban cores, medium-density residential areas, and low-density commercial zones, was selected based on stratified random sampling. Data collection involved deploying a multi-rotor UAV equipped with high-resolution RGB cameras and a terrestrial laser scanner with a calibrated 3D laser sensor. The UAV flights were conducted at altitudes averaging 50 meters, with ground control points established for georeferencing. TLS scans were executed from multiple stations to minimize occlusion and ensure comprehensive coverage. Data preprocessing encompassed image processing through Structure-from-Motion (SfM) workflows for UAV data and point cloud registration for TLS data, followed by mesh generation and spatial accuracy assessment. The analysis employed quantitative metrics such as Root Mean Square Error (RMSE), completeness, and density of the generated point clouds. Statistical techniques, including paired t-tests and ANOVA, were used to compare the accuracy and efficiency metrics between the two methods. Additionally, a cost-benefit analysis was conducted to evaluate operational costs and processing times. Qualitative assessments via stakeholder interviews complemented the quantitative analysis, shedding light on operational challenges and data usability. It is anticipated that the findings will demonstrate comparable spatial accuracies between UAV photogrammetry and TLS within certain urban features, with UAVs offering superior operational flexibility and rapid deployment capabilities, while TLS provides higher point density and detail in complex structures. The research expects to reveal that UAV systems are more cost-effective and suitable for rapid urban assessments, whereas TLS is preferable for detailed surveys of critical infrastructure zones requiring high precision. This study contributes substantive empirical data to the body of knowledge on urban 3D data acquisition, particularly in contrasting photogrammetric UAV and TLS methodologies. It provides a practical decision framework for urban planners, surveyors, and GIS professionals in choosing appropriate survey technologies based on project scope, accuracy requirements, and resource availability. The main conclusion advocates for integrated survey strategies that leverage the complementary strengths of UAV and TLS systems, tailored to specific urban mapping needs. Recommendations include optimizing data acquisition workflows, enhancing interoperability between datasets, and exploring emerging drone-based remote sensing innovations for urban environments. The findings are expected to influence future guidelines in urban geoinformatics practices and assist policymakers in resource allocation for urban spatial data infrastructure development.