Visible land boundaries allow for automatic detection using remote sensing data with optical sensors. The dissertation aimed to improve cadastral mapping using unmanned aerial vehicle (UAV) photogrammetry. The aim was to evaluate the accuracy of cadastral data concerning land boundaries and to develop an automated approach for delineating these boundaries. The data captured by UAVs was analyzed to identify discrepancies between physical (visible) and formal (cadastral) land boundaries. The process includes boundary detection, geo-referencing, evaluation of up-to-dateness, and vectorization of the predicted boundary maps. Initially, image processing methods were tested for automatic detection. Subsequently, deep learning methods were used to improve the detection process using UAV data. Manual delineations were also carried out to validate and assess the accuracy of the automated detections. Different approaches and methods were tested in case studies, especially in rural areas where visible land boundaries are more common. Although primarily tested with drones, it can also be adapted to satellite or aerial imagery and provides a cost-effective way to detect and revise cadastral maps. Automatic detection identifies areas needing cadastral updating and is supported by manual verification to ensure accuracy.Povzetek: Doktorska disertacija preučuje izboljšanje katastrskih načrtov z uporabo UAV fotogrametrije. To je razširjeni povzetek disertacije, katere cilj je bil raziskati neskladja med katastrskimi in dejanskimi mejami ter razviti pristop za posodobitev obstoječih katastrskih načrtov na podlagi podatkov iz UAV fotogrametrije.