In order to transcend the challenge of accelerating the establishment of cadastres and to efficiently maintain them once established, innovative, and automated cadastral mapping techniques are needed. The focus of the research is on the use of high-resolution optical sensors on unmanned aerial vehicle (UAV) platforms. More specifically, this study investigates the potential of UAV-based cadastral mapping, where the ENVI feature extraction (FX) module has been used for data processing. The paper describes the workflow, which encompasses image pre-processing, automatic extraction of visible boundaries on the UAV imagery, and data post-processing. It shows that this approach should be applied when the UAV orthoimage is resampled to a larger ground sample distance (GSD). In addition, the findings show that it is important to filter the extracted boundary maps to improve the results. The results of the accuracy assessment showed that almost 80% of the extracted visible boundaries were correct. Based on the automatic extraction method, the proposed workflow has the potential to accelerate and facilitate the creation of cadastral maps, especially for developing countries. In developed countries, the extracted visible boundaries might be used for the revision of existing cadastral maps. However, in both cases, the extracted visible boundaries must be validated by landowners and other beneficiaries.

The main goal of our research was to design and implement an innovative target that would be suitable for accurately registering point clouds produced from unmanned aerial vehicle (UAV) images and terrestrial laser scans. Our new target is composed of three perpendicular planes that combine the properties of plane and volume targets. The new target enables the precise determination of reference target points in aerial and terrestrial point clouds. Different types of commonly used plane and volume targets as well as the new target were placed in an established test area in order to evaluate their performance. The targets were scanned from multiple scanner stations and surveyed with an unmanned aerial vehicle DJI Phantom 4 PRO at three different altitudes (20, 40, and 75 m). The reference data were measured with a Leica Nova MS50 MultiStation. Several registrations were performed, each time with a different target. The quality of these registrations was assessed on the check points. The results showed that the new target yielded the best results in all cases, which confirmed our initial expectations. The proposed new target is innovative and not difficult to create and use.

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

Most cadastral systems today are coordinate-based and contain only a weak or no reference to measurements or the origin of the information. In some contexts, this is largely due to the transition of land data management and maintenance from an analogue to a digital environment. This study focuses on analysing the importance of the measurement-based cadastre and the digitisation process in North Macedonia and Slovenia. The survey-based boundary data and their integration into the digital environment were not considered in either case study. The positional differences between the survey-based boundary coordinates and the graphical coordinates of the boundaries are significant. The RMSE(2D) for Trebosh was 48 cm, and the RMSE(2D) for Ivanjševci was 56 cm. Consequently, the differences in location affected the areas of the cadastral parcels, resulting in an RMSE of 26 m² and 23 m² for Trebosh and Ivanjševci, respectively. These differences can be considered as differences within the cadastral boundary data. Therefore, before harmonising the data between the cadastre and the land register, the inconsistencies within the cadastral data should be eliminated first. The differences in the location of cadastral boundaries and parcel area create new challenges in cadastral procedures (formatting of parcels), conflicts in the relocation of boundaries, and impacts on the land market. The solution lies in the way data is maintained, avoiding duplication of attributes or eliminating inconsistencies (after duplication). Both solutions require further modifications of the legal framework for cadastral procedures related to boundary adjustments and data compliance. This study provides a basis for evaluating inconsistencies in cadastral data and highlights the importance of proper source data selection in the digitization process.