Sylwia Borkowska

The paper addresses the problem of weighting in an analysis that supports the selection of a categorical data set according to user needs. Using the Relative Change (RC) of the Compound Correspondence Index (CCI), it is shown that weights have a significant impact on user choice – reaching extreme values in both urbanized and forested areas. Decreasing the weights from 0.25 to 0.17 in forested and built-up areas resulted in the maximum variations that were seen in the hot spot maps, with cold areas generally corresponding to builtup regions and hot areas to forested areas. The analysis covers seven counties that are located in different regions of Poland: Pomerania, Podlasie, Mazovia, Greater Poland and the Beskidy Mountains.

The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail.

This study aims at a comparative analysis of quantitative data, namely, OSM and BDOT10k. Analyses were conducted in a 1 km² hexagonal grid, in seven test counties located in different regions of Poland, differing in the degree of urbanization, land cover and natural environment. It is assumed that the authors’ consolidated regional classification of the Compound Correspondence Index CCI_Rn is attributed to the geometric mapping unit based on TOPSIS values, and their statistical measure of dispersion enables the comparison of datasets for individual geographically disjointed areas according to uniform criteria, e.g., the number of topographic features stored in analyzed datasets, both polygonal (buildings, forests, surface water) and linear (roads, watercourses, railroads). The final results of the regional assessment outperform the local classification giving a higher level of data compliance. Overestimation of regional concordance ranges from 9 to 20% of the county area, with an average of 3% reduction in the area where the two datasets (BDOT10k and OSM) have comparable information ranges. Areas of medium and high nonconformity are decreased by an average of 2.4%.

One potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators is OpenStreetMap (OSM). The purpose of this paper is to provide a comprehensive evaluation of the spatial data quality elements of OSM against the national official data—the database of topographic objects at a scale of 1:10,000. Such spatial data quality elements as location accuracy, data completeness and attribute compatibility were analysed. In the conducted OpenStreetMap tests, basic land-cover classes such as roads, railroads, river network, buildings, surface waters and forests were analysed. The test area of the study consisted of five counties in Poland, which differ in terms of location, relief, surface area and degree of urbanization. The best results of the quality of OSM spatial data were obtained for highly urbanized areas with developed infrastructure and a high degree of affluence. The highest degree of completeness of OSM linear and area objects in the studied counties was acquired in Piaseczyński County (82%). The lowest degree of completeness of the line and area objects of OSM in the studied counties was obtained in the Ostrowski County (51%). The calculated correlation coefficient between the quality of OSM data and the income per capita in the county was 0.96. The study complements the previous research results in the field of quantitative analysis of the quality of OSM data, and the obtained results confirm their dependence on the geometric type of the analysed objects and characteristics of test areas, i.e., in this case counties in Poland. The obtained results of OSM data quality analysis indicate that OSM data may provide strong support for other spatial data, including official and state data. OSM stores significant amounts of geospatial data with relatively high data quality that can be a valuable source for monitoring some SDG indicators.

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.

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.

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).

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.

In the article was presented the issue of developing passability maps using high resolution spatial data. To carry out this task, the most detailed maps available in Poland were used: base map in scale 1:500, BDOT10k in scale 1:10 000 and high-resolutionDigital Terrain Elevation Data with spatial resolution 1 m × 1 m. The maps of passability were made using two methods: a standard method based on the division of terrain into three passability classes (GO, SLOW-GO and NO-GO TERRAIN) and the second method of using the Vegetation Roughness Factor. The second method is fully automatic and it makes enable to calculate the index of passability of the primary fields in various sizes. To carry out this task, the square primary fields with size 3, 5, 20, 50, 100 m have been used. Developed maps have been subjected to a comprehensive analysis and comparison. The results show, that the most detailed maps, which were developed for a primary field of size 3 to 10 meters, have visualized the most terrain obstacles. It predisposes them to be used in planning of military operations, crisis management or defining movement of the UGV (Unmanned Ground Vehicles).