Krzysztof Pokonieczny

Route planning through forested terrain is an important problem in military operations and crisis management, since it requires accounting not only for terrain morphology but also for the spatial distribution of individual trees and the characteristics of the moving vehicle. Existing trafficability models typically represent forest as a generalized land-cover class, which cannot capture the specific tree gaps and local slopes that determine whether a given vehicle can actually pass. This study proposes a routing method based on a Voronoi graph constructed from individual tree locations, in which each edge is characterized by length, terrain slope, clearance between neighboring trees, and canopy concealment, combined into a multi-criteria cost function that also penalizes vehicle turning. The graph is adapted to a specific vehicle by removing edges that violate its width or maximum slope constraints. The method was tested on four plots in Kampinos Forest, Poland, and five vehicle types. Feasible routes were obtained for all 20 area–vehicle combinations, with Dijkstra’s and A* algorithms returning identical solution costs, and route length exceeding the straight-line distance by a modest margin in most cases. Graph size scaled near-linearly with tree count. Vehicle width and slope tolerance affected feasibility and route length through separate mechanisms, while terrain scale governed computational cost. A sensitivity analysis showed that cost-function weights influence routes differently depending on terrain, and that the slope and concealment criteria interact rather than acting independently. The proposed approach enables vehicle-specific route modelling in forested terrain at the resolution of individual trees, rather than generalized land cover.

The study investigates the spatial variability of the cone index (CI) and its implications for vehicle mobility across two contrasting regions in Poland: the Suwalki Gap and Garwolin County. Background motivation stems from the need to assess off-road trafficability for agricultural, forestry, and other vehicles operating on soils whose strength varies seasonally and spatially. Using 230 penetrometric measurements collected with an electronic Penetrologger equipped with a soil moisture sensor, CI values were recorded to a depth of 80 cm and supported with soil–agricultural maps and Sentinel-2 land-cover data. Results demonstrate clear relationships between CI, soil moisture, land cover, soil type, and depth. Wetlands exhibited consistently low CI (<1 MPa), while agricultural, artificial, and forested areas showed increasing resistance with depth, surpassing 2 MPa in deeper layers. Seasonal differences were pronounced: summer drying increased surface CI, whereas autumn profiles were generally softer but more uniform. Regression analysis confirmed a strong negative correlation between soil moisture and CI, particularly below 20 cm. Comparative assessment with vehicle cone index thresholds indicates that most terrains are suitable for heavy vehicles, except saturated wetlands, which pose significant trafficability constraints. The findings emphasize the importance of depth-specific CI assessment, the strong influence of local soil disturbances, and the need for high-density measurements to support real-time mobility modelling for agricultural and crisis-management applications.

This study examines the effectiveness of various spatial data sources and pathfinding algorithms for route determination in forested environments, focusing on the Mazovia region of Poland. Accurate and efficient forest route planning is critical for both military operations and crisis management, highlighting the need for reliable data and robust algorithms. The analysis centers on three primary spatial data sources that can support forest routing: the civilian Topographic Objects Database (TOD) and OpenStreetMap (OSM), along with the military-specific Vector Map Level 2 (VML2). Two commonly used pathfinding algorithms, Dijkstra and A* (the latter with six heuristic variations), were tested to assess their suitability and performance in these contexts. This study was conducted across ten of the largest forested areas in Mazovia, with route determinations performed between selected pairs of start and end points within each forest area. The findings indicate that the TOD database yielded the most stable and consistent routes, while the A* algorithm with Euclidean distance heuristics proved to be the fastest among the tested variants. In contrast, OSM data presented challenges due to inconsistencies, resulting in some routes being undeterminable, where connections between start and end points were lacking. These results underscore the importance of data quality and algorithm selection in effective forest route planning.

This article addresses a significant aspect of evaluating and comparing spatial forest data from various databases developed and maintained in Poland and globally. The study focused on the application of these data to create terrain passability maps, which are useful in planning military and crisis operations. The research was conducted in a test area near Warsaw, encompassing the Kampinos Forest. In the study, the “forest” layers from the tested databases were compared. Their spatial extents were analyzed, and terrain passability maps were generated in different configurations, which were comprehensively compared with one another. The results indicated that the quality and detail of forest data are not critical for generating passability maps. Only in the case of creating highly detailed maps does the use of precise data prove justified. As the level of detail in the maps decreases, they become increasingly similar, reducing the influence of the forest data on their accuracy and operational applicability. The study enabled the selection of the most accurate data sources on forested areas—those that most faithfully represent the structure of forested regions in Poland.

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%.

Determination of the movement route for troops is a key element of the Intelligence Preparation of the Battlefield (IPB). In this article, the authors present a methodology of using terrain passability maps for generating graphs that enable the determination of the optimum route between two points. The proposed methodology enables the determination of different variants of routes: a longer route that passes all terrain obstacles or a route that is shorter but more difficult to pass. The study presents the possibility of implementing the proposed methodology for the movement of military tactical compounds that move inside a 500 m wide corridor. The selected test area was the Polish coast of the Baltic Sea. The analyses were conducted with the use of level 2 Vector Map.