A Framework for Integrating UAV Data in Urban Land Use Planning

Thesis Abstract

Urban land use planning increasingly requires precise, current, and spatially detailed data to support sustainable development and effective decision-making. However, traditional data acquisition methods such as ground surveys and aerial photography often face limitations in terms of cost, timeliness, and spatial resolution. Recent advancements in Unmanned Aerial Vehicle (UAV) technology offer a promising alternative for acquiring high-resolution geospatial data efficiently. Despite the potential benefits, there is a lack of a comprehensive framework that systematically integrates UAV-derived data into existing land use planning workflows, hindering operational adoption by planning authorities. This study aims to develop a robust, adaptable framework for integrating UAV data into urban land use planning processes, thereby bridging the gap between technological capabilities and practical applications. The specific objectives of the research include (i) assessing the current state of UAV technology and its applications in urban land use planning; (ii) identifying the data requirements and processing workflows suitable for urban contexts; (iii) designing an integration framework that encompasses data acquisition, processing, analysis, and interpretation; and (iv) validating the proposed framework through a case study of urban land parcels in a mid-sized city with a sample of 150 land plots selected via stratified random sampling. The study adopts a mixed-methods research design, combining qualitative approaches—such as expert interviews (n=12) with urban planners and GIS professionals—and quantitative methods, including spatial data analysis and model validation. Data collection involves UAV flights conducted over the selected urban area, using multispectral and high-resolution RGB sensors to generate photogrammetric point clouds, orthomosaics, and 3D models. Complementary data sources include existing cadastral maps, land use maps, and socioeconomic data obtained from municipal planning departments. The UAV data are processed using Pix4D and DroneDeploy software for photogrammetric reconstruction, followed by spatial analysis in ArcGIS Pro and QGIS. To ensure data quality, validation procedures include ground control points (GCPs) and accuracy assessments such as Root Mean Square Error (RMSE) analysis. The analysis employs descriptive statistical techniques to characterize data accuracy, while inferential methods, including regression analysis, are used to test the influence of UAV-derived metrics on land use classification accuracy. The study also explores the application of a modified version of the Weintraub and Kroll’s (1980) model for integrating spatial data into land use planning workflows, adapted to incorporate UAV outputs. The framework’s practical effectiveness is evaluated through comparison with conventional data sources, assessing factors such as cost, temporal efficiency, and spatial resolution improvements. Expected findings indicate that UAV-derived data significantly enhance the granularity and timeliness of spatial information, leading to more accurate land use maps and improved planning decisions. The research anticipates demonstrating that the proposed framework facilitates seamless data flow and integration between UAV outputs and existing GIS systems, thereby optimizing land use planning processes. Moreover, the validation exercises are expected to confirm the framework’s applicability across comparable urban environments, with a quantifiable improvement in data reliability and operational efficiency. This study contributes to the knowledge base by providing a systematically developed, practically applicable framework for UAV data integration, addressing a critical gap in urban planning literature and practice. It extends the theoretical understanding of UAV application within spatial planning paradigms, incorporating concepts from the Theory of Technological Acceptance and Urban Planning Decision Support Systems. The findings are expected to inform policymakers, urban planners, and GIS practitioners on optimal UAV data utilization strategies, fostering more effective, timely, and sustainable urban development. In conclusion, the research recommends establishing standardized UAV data acquisition and processing protocols within urban planning institutions, fostering capacity building in UAV technology, and promoting further empirical studies across diverse urban contexts to enhance the framework's adaptability and robustness under varying regulatory and environmental conditions.