Detecting land cover changes requires timely and accurate information, which can be assured by using remotely sensed data and Geographic Information System(GIS). This paper examines spatiotemporal trends in land cover changes in the Polish Baltic coastal zone, especially the urbanisation, loss of agricultural land, afforestation, and deforestation. The dynamics of land cover change and its impact were discussed as the major findings. The analysis revealed that land cover changes on the Polish Baltic coast have been consistent throughout the 1990–2018 period, and in the consecutive inventories of land cover, they have changed faster. As shown in the research, the area of agricultural land was subject to significant change, i.e., about 40% of the initial 8% of the land area in heterogeneous agriculture was either developed or abandoned at about equal rates. Next, the steady growth of the forest and semi-natural area also changed the land cover. The enlargement of the artificial surface was the third observed trend of land cover changes. However, the pace of land cover changes on the Baltic coast is slightly slower than in the rest of Poland and the European average. The region is very diverse both in terms of land cover, types of land transformation, and the pace of change. Hence, the Polish national authorities classified the Baltic coast as an area of strategic intervention requiring additional action to achieve territorial cohesion and the goals of sustainable development.

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Applying georadar (GPR) technology for detecting underground utilities is an important element of the comprehensive assessment of the location and ground infrastructure status. These works are usually connected with the conducted investment processes or serialised inventory of underground fittings. The detection of infrastructure is also crucial in implementing the BIM technology, 3D cadastre, and planned network modernization works. GPR detection accuracy depends on the type of equipment used, the selected detection method, and external factors. The multitude of techniques used for localizing underground utilities and constantly growing accuracy demands resulting from the fact that it is often necessary to detect infrastructure under challenging conditions of dense urban development leads to the need to improve the existing technologies. The factor that motivated us to start research on assessing the precision and accuracy of ground penetrating radar detection was the need to ensure the appropriate accuracy, precision, and reliability of detecting underground utilities versus different methods and analyses. The results of the multi-variant GPR were subjected to statistical testing. Various analyses were also conducted, depending on the detection method and on the current soil parameters using a unique sensor probe. When planning detection routes, we took into account regular, established grids and tracked the trajectory of movement of the equipment using GNSS receivers (internal and external ones). Moreover, a specialist probe was used to evaluate the potential influence of the changing soil conditions on the obtained detection results. Our tests were conducted in a developed area for ten months. The results confirmed a strong correlation between the obtained accuracy and the measurement method used, while the correlation with the other factors discussed here was significantly weaker.

Geodetic networks provide a spatial reference framework for the positioning of any geographical feature in a common and consistent way. An even spatial distribution of geodetic control points assures good quality for subordinate surveys in mapping, cadaster, engineering activities, and many other land administration-oriented applications. We investigate the spatial pattern of geodetic control points based on GIScience theory, especially Tobler's Laws in Geography. The study makes contributions in both the research and application fields. By utilizing Average Nearest Neighbor, multi-distance spatial cluster analysis, and cluster and outlier analysis, it introduces the comprehensive methodology for ex post analysis of geodetic control points' spatial patterns as well as the quantification of geodetic networks' uniformity to regularly dense and regularly thinned. Moreover, it serves as a methodological resource and reference for the Head Office of Geodesy and Cartography, not only the maintenance, but also the further densification or modernization the geodetic network in Poland. Furthermore, the results give surveyors the ability to quickly assess the availability of geodetic points, as well as identify environmental obstacles that may hamper measurements. The results show that the base geodetic control points are evenly dispersed (one point over 50 sq. km), however they tend to cluster slightly in urbanized areas and forests (1.3 and 1.4 points per sq. km, respectively).Hence, the base geodetic control points (thereinafter refed as BGCPs) are of utmost importance for georeferencing of manuscripts of historical maps [11,18,19] or images from unmanned aerial vehicles [20].Geodetic control points should cover an area relatively evenly to enable accurate and cost-effective measurements [21][22][23]. Although there is a variety of studies considering the design and densification of geodetic control networks [16,24,25] as well as investigating the influence of topographic objects that hinder the visibility of the horizon, interfere with satellite signals, and, finally, affect the quality of geodetic control stations' positioning [26][27][28], a profound analysis of the spatial pattern of geodetic control points still requires investigation. The problem of geospatial distribution of geodetic control points was previously discussed in several publications [29][30][31]. However, these studies were concerned with the detail (third-order) of geodetic network point analyses in relatively small (less than 200 sq. km) rural areas. The results, related to surveying units and then grouped according to land use types, showed that geodetic control points are scattered with significantly visible groupings along roads, railways, and built-up areas. Moreover, the number and density of geodetic control points depend on the development of the area in question, and 35% to 50% depend on the land cover, mainly in locations of built-up areas, roads, and railways [30,31].The density of geodetic control points is specified by the National Map...

The purpose of this article was to present the methodology which enables automatic map labelling. This topic is particularly important in the context of the ongoing research into the full automation of visualization process of spatial data stored in the currently used topographic databases (e.g. OpenStreetMap, Vector Map Level 2, etc.). To carry out this task, the artificial neural network (multilayer perceptron) was used. The Vector Map Level 2 was used as a test database. The data for neural network learning (the reference label localization) was obtained from the military topographic map at scale 1 : 50 000. In the article, the method of applying artificial neural networks to the map labelling is presented. Detailed research was carried out on the basis of labels from the feature class “built-up area”. The results of the analyses revealed that it is possible to use the artificial intelligence computational methods to automate the process of placing labels on maps. The results showed that 65% of the labels were put on the topographic map in the same place as in the case of the labelling which was done manually by a cartographer. The obtained results can contribute both to the enhancement of the quality of cartographic visualization (e.g. in geoportals) and the partial elimination of the human factor in this process. Highlights for public administration, management and planning: • Map label placement is among key variables ensuring the usability of topographic maps across disciplines. • We present the neural network approach for automating the process of labelling topographic maps with locality names. • The presented case study applies to the military map in scale 1:50 000, but can be applied on other maps and geoportals.