Accurate positioning has become an essential component of modern life, crucial for applications ranging from navigation and industrial operations to emergency response. The Global Navigation Satellite System (GNSS) has traditionally provided reliable positioning, but its effectiveness diminishes in environments where satellite signals are obstructed, such as dense urban areas and indoor spaces. This thesis explores the potential of Fifth-Generation (5G) wireless communication technology, specifically utilizing Received Signal Strength Indicator (RSSI) data for positioning, as an alternative to GNSS. The research investigates the effectiveness of 5G positioning through trilateration and compares it with GNSS-Real-Time Kinematic (RTK) positioning. The study aims to validate the accuracy and the reliability of 5G positioning and various real-world scenarios, focusing on challenging environments. Key aspects examined include the impact of topography on positioning accuracy and the influence of network distribution on Position Dilution of Precision (PDOP). By attaching a 5G modem to a laptop, field measurements were collected and analyzed against the "ground truth" provided by GNSS-RTK. The results demonstrate the potential of 5G RSSI-based positioning to serve as a robust positioning solution. This study’s findings hold significant relevance for the geomatics community, with implications for urban planning, infrastructure development, environmental monitoring, and disaster management. Through critical analysis and validation, this thesis contributes to the advancement of positioning technologies, highlighting the limitations of 5G trilateration using RSSI, yet proposing it as a potential complement to GNSS. The findings pave the way for future research and practical applications in enhancing precise positioning systems.

Modern navigation heavily relies on Global Navigation Satellite Systems (GNSS) and digitized road network databases, but faces limitations in GNSS-denied areas and complex 2D road netowrks. This project addresses these challenges by developing a methodology to create and store a comprehensive 3D road and terrain dataset for enhanced navigation. In collaboration with TomTom, a company that aims to fulfill software requirements, making significant advancements in geolocation technology and societal contributions. The main research question of the project is: ”How can we create a 3D map of roads using information about the center of the road and elevation data?”. The approach to answer this question involves extracting 2D road polygons from centerline data based on width of the roads, the direction and the amount of lanes of them. These 2D polygons undergo enrichment with elevation data, with techniques like filtering, segmentation, and primitive extraction ensuring alignment with the digital terrain model. The methodology encompasses data acquistion, creation of polygons using the centerlines dataset, 2D-to-3D polygon conversion, elevation integration and data storage in CityJSON format.