Abstract Contemporary building documentation increasingly relies on laser scanning technologies that provide rapid and precise spatial data acquisition. Portable LiDAR systems such as the Leica BLK360, as well as mobile devices like the iPad, offer modern tools for efficient documentation of interior spaces, including office rooms. With dedicated applications (e.g., Leica Cyclone Field 360, Leica Cyclone 3DR, MagicPlan, BIMx, AutoCAD Mobile), users can record measurements, create sketches, and generate 3D models and photographic documentation within a single digital environment. The purpose of this study is to assess the dimensional accuracy in the documentation of an office space using portable LiDAR systems. The experiments showed that, for most analysed dimensions, the results agreed within several centimetres, which is sufficient for architectural inventory work. However, the issue of rounded corners was identified, which may significantly influence measurement results depending on the distance-measurement method applied. Reliable accuracy analysis required proper mutual alignment (common georeferencing) of the scans acquired with the selected instruments. To achieve this, point cloud classification was performed to identify surfaces suitable for cloud-to-cloud alignment. A predefined AI-based classification model dedicated to indoor environments was used. The findings confirm that portable LiDAR systems significantly reduce the time required to complete inventory tasks and enable more comprehensive visualisation of interior spaces. This technology serves as an effective tool supporting the design, modernisation, and management of office environments in a digital workflow, although its accuracy-related limitations must be considered.

Abstract Road upgrading to increase freight and passenger transport capacity requires surveying services, carried out at every stage of construction. During the construction of road sections designed as curves or arcs, their shape must be constantly monitored. During surveying, it is necessary to determine the elements of curves, in particular the radius and length of the curve, as well as the bisector, tangents, and angle of return of the tangents. The article presents a methodology for determining the radii of curves on highways using a GNSS system receiver with planar rectangular coordinates derived through post-processing using the Trimble Geomatics Office (TGO) software. In addition, the authors present the results of measurements of the height markers of the bottom layer of the substructure on the section from measuring point 1015+40 to measuring point 1020+00 of the Astana-Petropavlovsk highway. On the test object, tests were carried out on the technical condition of the pavement. The results were determined by the parameter. The highway section was also examined geometrically on the basis of levelling measurements. The paper proposes a methodology for road construction work that divides the measurements into two stages: initial — based on GNSS technology, and a second one — based on the levelling method.

Abstract Developing and improving environmental audit methodologies is integral to natural resource management. This paper presents an innovative approach to studying and analysing anthropogenic impacts on the territories whose boundaries are limited by the watershed area of a small or medium-sized river. Using geoinformation technologies and high-resolution digital elevation models allowed us to create an accurate geospatial model of anthropogenic transformations of the Oril River watershed area within the Dnipropetrovsk region (Ukraine). The proposed approach to identifying potentially hazardous objects is based on surface slopes and their distance from the riverbed within the watershed area. Based on the modelling results, a refined watershed area of the Oril River with a total area of 11.1 thousand square kilometres was developed. Digitalisation and categorisation of anthropogenically transformed lands allocated for residential development, industrial enterprises, farms, agricultural land, roads, and railwayswithin the Oril River watershed in the Dnipropetrovsk region (3963 km2) were conducted. It was found in the investigation that 79% of the study area comprises anthropogenically transformed land primarily used for agricultural and industrial activities. The modelling results underscore that agricultural activity serves as the principal source of anthropogenic pressure on the ecosystems of the studied area. The proposed improvements to the environmental audit methodology can be used in developing regulations to limit the negative impact of economic activity on the environment. This will ensure the region’s sustainable development and fulfil Ukraine’s obligations to preserve natural resources including international standards. The conclusions of the article point to the need to further improve the tools for assessing anthropogenic impact and planning environmental protection measures.

Abstract Assessing the reliability of using open-source software packages for post-processing of the Global Navigation Satellite System (GNSS) is essential since GNSS modernization has the potential to assist satellite navigation users. The purpose of this study is to evaluate the accuracy of using two MATLAB-based programs, raPPPid and PPPH for precise point positioning in Europe. Data from 12 MGEX stations over two days were used, with one day dedicated to each of the 6 stations and a 30-second observation interval. The data were post processed by PPPH and raPPPid programs and a comparison was made to evaluate the results accuracy produced by each software and the ones acquired from MGEX stations. Convergence time was also estimated. By comparing the root mean square error (RMSE) values for North, East and Up directions estimated by PPPH and raPPPid, it was found that raPPPid gives more accurate results where the RMSE in N direction estimated by raPPPid varied from 0.5 cm to 1.9 cm; however, RMSE in N direction estimated by PPPH varied from 0.7 cm to 2.8 cm. RMSE in E direction estimated by raPPPid varied from 0.4 cm to 3.3 cm, but RMSE in E direction estimated by PPPH varied from 0.5 cm to 3.7 cm. RMSE in Up direction estimated by raPPPid varied from 0.8 cm to 5.2 cm, while RMSE in Up direction estimated by PPPH varied from 0.9 cm to 5.5 cm. 3D Positioning error was also estimated by both software and it was found that the 3D positioning error estimated by raPPPid varied from 0.2 cm to 2.2 cm, whereas the 3D positioning error estimated by PPPH varied from 0.9 cm to 4.1 cm. Finally, the average convergence time achieved by raPPPid was 16.5 minutes, while the average convergence time achieved by PPPH was 32 minutes.

Abstract The recent research has demonstrated both the potential of building information management (BIM) for the 3D cadastre, especially for legal spaces of building units and the advantage of the combination of physical and legal boundaries for their visualization. During the lifetime cycle of the building and the units within, the geometry of units can change. Then, it is necessary to promote the transition from the updated BIM model to connected systems. Considering this, the paper focuses on a unique identification of building units between BIM and GIS. The main idea is that each feature (in our case, the building unit) receives a unique identifier when it is created, which is then used in every system during its life cycle. Each of these domains has its own specific technical requirements for the unique identification. The aim is to have just one identifier of the building unit regardless of the selected information system to make an effective update. The paper further aims to model the 3D legal spaces of building units using BIM and their storage inside the external spatial database based on the CityGML standard data model. The proposed solution facilitates the storage of the 3D legal spaces of building units together with their physical counterparts in one environment based on the widely recognized OGC standard. Furthermore, as many countries still use 2D parcel-based cadastre, the proposed solution also allows for the connection of the existing cadastral information systems with these externally stored 3D legal spaces without the necessity of changing the systems.

Abstract The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. Several variants of each algorithm were tested, adopting different parameters typical for each of them. Each variant was classified multiple (20) times, using training samples of different sizes: from 100 pixels to 200,000 pixels. The tests were conducted independently on 3 Sentinel-2 satellite images, identifying 5 basic land cover classes: built-up areas, soil, forest, water, and low vegetation. Typical metrics were used for the accuracy assessment: Cohen’s kappa coefficient, overall accuracy (for whole images), as well as F-1 score, precision, and recall (for individual classes). The results obtained for different images were consistent and clearly indicated an increase in classification accuracy with the increase in the size of the training sample. They also showed that among the tested algorithms, the XGB algorithm is the most sensitive to the size of the training sample, while the least sensitive is SVM, which achieved relatively good results even when using training samples of the smallest sizes. At the same time, it was pointed out that while in the case of RF and XGB algorithms the differences between the tested variants were slight, the effectiveness of SVM was very much dependent on the gamma parameter – with too high values of this parameter, the model showed a tendency to overfit, which did not allow for satisfactory results.

Abstract The field of satellite navigation has seen significant advancements due to the fast development of multi-constellation Global Navigation Satellite Systems (GNSS). Around 150 satellites will be in service when all six systems – GPS, GLONASS, Galileo, BeiDou, QZSS, and NAVIC – are launched by 2030, offering both enormous potential and advantages for research and engineering applications. This study used an experiment on the accuracy, particularly for short, medium, long baselines (Wide Lane ambiguity solution) of the BeiDou, QZSS and QZSS/BeiDou combinations. It showed that with the integration of BeiDou/QZSS static measurements in the study region millimetre-centimetre accuracy for short, medium, and long baselines can be attained. Based on the results of this study, it can be concluded that the 1st (QZSS/BeiDou), 2nd (BeiDou), and 3rd (QZSS) strategies feature different horizontal accuracies for all categories. The obtained results with different satellite configurations for the Fixed-Wide-Lane integer ambiguity solution are compared with each other. Accuracy at the short baseline (BeiDou, QZSS, and BeiDou/QZSS satellites) was obtained in the range of 0.5–0.7 cm. For the medium baseline, it was computed around 1.8–82 cm. For the long baseline, the accuracy was 5.6–13.3 cm.

Abstract The “Kłodawa” salt mine, due to geological conditions and continuous salt extraction, is subject to a range of measurements documenting the speed of changes in the geometry of the chambers. Cyclic surveys are conducted under challenging conditions several hundred metres underground. Consequently, measurement methods used for determining the parameters of the ongoing clamping should be of high precision but also be resistant to dense dust (in fields of active mining) and strong gusts (near ventilation shafts). The research presented here concerns the analysis of the possibilities of solutions offered by modern technologies in mine conditions. Test measurements were conducted at observation stations using linear bases stabilized with metal pins. The base points were located in the aisles, ceiling, and bottom of the chamber in Field 1 of “Kłodawa” salt mine at the depth of 600m. Point clouds mapping the object were acquired using a Leica RTC360 3D laser scanner and two mobile devices: Motorola G100 smartphone and iPad Pro with LiDAR technology using the Pix4Dcatch application. The accuracy of the point cloud from the Leica RTC360 3D laser scanner was determined by comparing it with classic measurements taken with a Leica Disto laser rangefinder. The repeatability and accuracy of the point cloud from a smartphone were examined using statistical analysis based on Pearson’s correlation coefficient and cross-correlation. An attempt was also made to approximate the correlation between the obtained errors and two parameters: the number of images and the size of the object.

Abstract This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43ms for the 10th day and from 3.07 ms to 8.05ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.

Abstract Two-dimensional interpolation – or surface fitting – is an approximation tool with applications in geodetic datum transformations, terrain modelling and geoid determination. It can also be applied to many other forms of geographic point data, including rainfall, chemical concentrations and noise levels. The problem of fitting of a smooth continuous interpolant to a bivariate function is particularly difficult if the dataset of control points is scattered irregularly. A typical approach is a weighted sum of data values where the sum of the weights is always unity. Weighting by inverse distance to a power is one approach, although a power greater than 1 is needed to ensure smooth results. One advantage over other methods is that data values can be incorporated into the interpolated surface. One disadvantage is the influence of distant points. A simple cut-off limit on distance would affect continuity. This study proposes a transition range of accelerated decline by means of an adjoining polynomial. This preserves smoothness and continuity in the interpolating surface. Case studies indicate accuracy advantages over standard versions of inverse-distance weighting.