Changes of the antenna models on permanent global navigation satellite system (GNSS) stations can lead to jumps and discontinuities in the coordinate time series. In this paper, the results of research on the adequacy of the antenna phase centre corrections (PCC) variations are presented by analysing its component—the antennas’ phase centre offset (PCO). For this purpose, height differences were determined using different and independent methods: EUREF Permanent Network (EPN) combined solutions, Precise Point Positioning (PPP), and the single baseline solution. The results of GNSS processing were referenced to direct geometric levelling outputs. The research was conducted only within the global positioning system (GPS) system due to the compatibility of one of the receivers, and the experiment was based on a comparison of the height differences between four GNSS antennas located on the roof of a building: two permanent station antennas and two auxiliary points. The antennas were located at similar heights; precise height differences were determined by geometric levelling, both at the beginning and the end of the session. Post-processing was conducted with the use of the GPS system, precise ephemeris, the adopted antenna correction model, and a zero-elevation mask. For one of the antennas, a change of the antenna characteristic model from IGS08 to IGS14 leads to an 8-mm difference in height. Older antennas used in the national (or transnational) permanent network need individual PCC.

Currently, in Poland, the State Spatial Reference System (SSRS) includes two height reference frames: PL-KRON86-NH and PL-EVRF2007-NH as the Polish implementation of the European Vertical Reference System (EVRF). The deadline for EVRF2007 implementation in Poland, as the only legally permitted height frame, is set at the end of 2023. Because of the administrative division, the implementation was planned in two stages: at the state level (government) and the local level (districts). In the article, the local ones are reviewed, especially regarding the aim of height conversion methods, costs and their progress. As a background, a historical perspective of height systems and frames used in Poland since the beginning of the 20th century is provided. The source of data was the Head Office of Geodesy and Cartography (HOGC), as well as the district surveying departments’ official answers; open documents posted on the Internet, e.g. tender documentation and their results were also analysed.

The utilization of augmented reality (AR) is becoming increasingly prevalent in the integration of virtual reality (VR) elements into the tangible reality of the physical world. It facilitates a more straightforward comprehension of the interconnections, interdependencies, and spatial context of data. Furthermore, the presentation of analyses and the combination of spatial data with annotated data are facilitated. This is particularly evident in the context of mobile applications, where the combination of real-world and virtual imagery facilitates enhances visualization. This paper presents a proposal for the development of a multimodal system that is capable of identifying roof types in real time and visualizing them in AR on mobile devices. The current approach to roof identification is based on data made available by public administrations in an open-source format, including orthophotos and building contours. Existing computer processing technologies have been employed to generate objects representing the shapes of building masses, and in particular, the shape of roofs, in three-dimensional (3D) space. The system integrates real-time data obtained from multiple sources and is based on a mobile application that enables the precise positioning and detection of the recipient’s viewing direction (pose estimation) in real time. The data were integrated and processed in a Docker container system, which ensured the scalability and security of the solution. The multimodality of the system is designed to enhance the user’s perception of the space and facilitate a more nuanced interpretation of its intricacies. In its present iteration, the system facilitates the extraction and classification/generalization of two categories of roof types (gable and other) from aerial imagery through the utilization of deep learning methodologies. The outcomes achieved suggest considerable promise for the advancement and deployment of the system in domains pertaining to architecture, urban planning, and civil engineering.

The study focuses on predicting cycle path development for the year 2030 in Lublin by integrating land cover data with key factors such as demographic trends, cycling path design guidelines, and topographic features. The authors propose a novel method for identifying optimal locations for new bike paths, emphasizing the prioritization of routes, based on a demographic index across Lublin’s districts and on land cover forecasts for 2030. This forward-looking approach ensures that the proposed network aligns with future urban development and population distribution patterns, creating a cycling infrastructure that is both adaptable and responsive to the city’s evolving needs. As a result, the study designates areas of approximately 30 km ² for the development of cycle tracks, creating a comprehensive and flexible cycle infrastructure that adapts to projected changes in land use.