This doctoral dissertation deals with spatial data quality monitoring and assurance, more specifically with the quality of data in the process of 3D building modelling from photogrammetric point cloud obtained from imagery acquired by an unmanned aerial vehicle (UAV). As UAVs are increasingly used to acquire spatial data, ensuring spatial data quality is an emerging relevant topic. Based on the literature review, we thoroughly analysed the procedures for spatial data acquisition, processing and modelling acquired by a UAV to obtain a topologically correct 3D building model in a vector form. We developed a process model to identify critical factors that affect data quality throughout data processing and modelling. Based on the identified factors, we designed a conceptual model for spatial data quality assurance within the whole process. Considering the conceptual model, the process model has been expanded by integrating data quality procedures for controlling the quality of intermediate results within the 3D building modelling process. The developed process model, which includes both data processing and quality managing, is an important research achievement that transparently presents all data-modelling phases from the UAV data acquisition to the final 3D building model. In the experimental part, the conceptual and process model have been verified for datasets in two study areas. Here, we have further analysed the influence of selected factors on the quality of the intermediate results of data processing and the final 3D building model. Besides the comprehensive study of 3D building modelling based on UAV data and a detailed process model for data modelling, the research contributions are to be seen in the impact analysis of the selected factors and verification of the detailed process model with data-quality monitoring.