Comparative Analysis of Drone and UAV-Based Land Parcel Mapping Accuracy

Thesis Abstract

Accurate land parcel mapping is critical for effective land management, urban planning, and cadastral documentation, yet there remains a significant gap in understanding the comparative performance of emerging unmanned aerial vehicle (UAV) technologies, specifically drones, versus traditional aerial survey platforms. This study aims to evaluate and compare the positional accuracy, measurement reliability, and spatial resolution of land parcel maps generated by drone-based and UAV-based mapping systems, with the broader objective of identifying the most effective approach under varying environmental and operational conditions. The specific objectives include quantifying the positional deviations of land parcel boundaries captured by both platforms, analyzing the cost-effectiveness and operational efficiency of each method, and assessing the impact of environmental variables such as topography and vegetation cover on mapping accuracy. The research adopts a mixed-methods comparative cross-sectional design, integrating quantitative accuracy assessment with qualitative evaluation of operational usability. The population consists of 200 land parcels located within a peri-urban region characterized by heterogeneous land use, topography, and vegetation cover, selected through stratified random sampling to ensure representativeness. A sample of 100 land parcels was surveyed using both drone and UAV platforms within a three-month period, with data collected via high-resolution multispectral and RGB cameras mounted on each platform. Control points established with high-precision GNSS static surveys served as reference standards for accuracy assessments. Quantitative data analysis involved georeferencing the captured imagery using Photogrammetric software, followed by spatial accuracy assessment through root mean square error (RMSE) analysis and paired t-tests to identify statistically significant differences in positional accuracy. An analysis of variance (ANOVA) was conducted to compare accuracy across different land cover types and topographical conditions. The study also incorporated cost-benefit analysis and operational efficiency metrics, including survey time, personnel requirements, and equipment costs, using descriptive and inferential statistics. Qualitative insights into operational usability and user experience were gathered through structured interviews with survey practitioners and analyzed thematically. Preliminary expected findings suggest that drone-based mapping systems exhibit superior spatial resolution and can achieve higher positional accuracy with RMSE values within 5 centimeters, outperforming traditional UAV platforms under optimal conditions. However, the results may indicate that UAV-based platforms with stabilized mounts and advanced GNSS receivers maintain comparable accuracy levels, particularly in challenging terrains. The study anticipates identifying critical environmental and operational factors influencing data quality, and establishing a cost-effectiveness threshold favoring drone systems for rapid survey deployment in peri-urban settings. This research contributes to the existing body of knowledge by providing a comprehensive, empirically validated comparison of drone and UAV-based land parcel mapping accuracies in diverse conditions, incorporating both technical and operational dimensions. It advances the understanding of the practical applicability of these technologies in land management and cadastral surveying, while offering actionable guidelines for practitioners and policymakers on selecting appropriate mapping platforms based on accuracy requirements, environmental conditions, and resource constraints. The main conclusion emphasizes that drone systems are generally more accurate and operationally efficient for land parcel mapping in peri-urban environments, particularly when equipped with high-precision GNSS modules, although the choice of technology should be context-dependent. Recommendations include integrating drone-based methods into existing cadastral workflows, investing in capacity building for drone operation, and further exploring the integration of data from multiple UAV platforms to enhance mapping precision in complex terrains. Future studies are suggested to evaluate long-term reliability and the potential for automation in land parcel mapping workflows.