The article presents a comprehensive analysis of the possibilities of using Geographic Information System (GIS) tools based on artificial intelligence (AI) for vectorizing utility networks from existing analog cartographic materials, as well as integrating the obtained data with the results of topographic measurements performed by geodetic methods directly in the field. The study examines current trends in the development of geodetic equipment, including multifunctional devices such as the Leica Nova MS60 MultiStation, Leica GS18 I GNSS receiver, and Leica RTC360 laser scanner, which allow for extensive high-precision spatial measurements with a minimal number of personnel. Special attention is given to the challenges of office processing of large datasets obtained during fieldwork, as well as the issues of accuracy and updating information about utility networks amid rapid urban infrastructure development. The study analyzes existing problems related to the fragmentation and obsolescence of analog materials, the lack of a unified coordinate system, data duplication, and low digitalization of utility networks. It demonstrates that using AI-based tools, such as the Raster Tracer and Bunting Labs AI Vectorizer plugins in QGIS, can significantly accelerate the vectorization process and improve the quality of the resulting data through semi-automatic tracing of network lines and contours. At the same time, the necessity of monitoring the process, making manual corrections, and using geometry simplification tools after tracing is emphasized. A methodology for processing spatial data of utility networks has been developed, including the collection of topographic and technical materials, identification of key objects in the field, use of modern devices for coordinate determination and creation of digital models, as well as integration of data into GIS and BIM systems. The study concludes that the integrated use of geodetic methods, GIS, and AI-based tools significantly enhances the efficiency of data collection and processing, ensures the updating of utility network databases, improves the accuracy of spatial information, and lays the foundation for further integration into smart city systems. The research methods applied include measurement, comparison, analysis, sampling, synthesis, and modeling.
M. Kukhar (Fri,) studied this question.