The determination of the route of movement is a key factor which enables navigation. In this article, the authors present the methodology of using different resolution terrain passability maps to generate graphs, which allow for the determination of the optimal route between two points. The routes are generated with the use of two commonly used pathfinding algorithms: Dijkstra’s and A-star. The proposed methodology allows for the determination of routes in various variants—A more secure route that avoids all terrain obstacles with a wide curve, or a shorter route, which is, however, more difficult to pass. In order to achieve that, two functions that modify the value of the index of passability (IOP), which is assigned to the primary fields that the passability map consists of, have been used. These functions have a β parameter that augments or reduces the impact of the applied function on IOP values. The paper also shows the possibilities of implementation of the methodology for the movement of single vehicles or unmanned ground vehicles (UGVs) by using detailed maps as well as for determining routes for large military operational units moving in a 1 km wide corridor. The obtained results show that the change in β value causes the change of a course of the route as expected and that Dijkstra’s algorithm is more stable and slightly faster than A-star. The area of application of the presented methodology is very wide because, except for planning the movement of unmanned ground vehicles or military units of different sizes, it can be used in crisis management, where the possibility of reaching the area outside the road network can be of key importance for the success of the salvage operation.

In this article, we discuss issues concerning the development of detailed passability maps, which are used in the crisis management process and for military purposes. The paper presents the authorial methodology of the automatic generation of these maps with the use of high-resolution digital elevation models (DEMs) acquired from airborne laser scanning (light detection and ranging (LIDAR)) and photogrammetric data obtained from unmanned aerial vehicle (UAV) measurements. The aim of the article is to conduct a detailed comparison of these models in the context of their usage in passability map development. The proposed algorithm of map generation was tested comprehensively in terms of the source of the used spatial data, the resolution, and the types of vehicles moving in terrain. Tests were conducted on areas with a diversified landform, with typical forms of relief that hinder vehicle movement (bluffs and streams). Due to the huge amount of data to be processed, the comprehensive analysis of the possibilities of using DEMs in different configurations of pixel size was executed. This allowed for decreasing the resolution of the model while maintaining the appropriate accuracy properties of the resulting passability map. The obtained results showed insignificant disparities between both sources of used DEMs and demonstrated that using the model with the 2.5 m pixel size did not significantly degrade the accuracy of the passability maps, which has a huge impact on their generation time.

The article presents a methodology to determine access routes to places with difficult access, situated outside the regular transport network. To determine the optimum route, the existing road network was used, along with terrain passability maps, as well as high-resolution digital terrain models together with the traction parameters of the vehicle to enable conducting a detailed analysis of micro-relief and excluding inaccessible areas. The research was extended to include a comprehensive analysis of terrain accessibility for areas located in the vicinity of the capital cities of three European countries on various levels of economic development: Germany, Poland, and Ukraine. The degree of development of the analysed area was determined with the use of the gross domestic product (GDP), human development index (HDI), and the road quality coefficient. The case study presented in the paper illustrates the practical application of the developed methodology that was implemented to a real crisis situation. Access routes were determined for a MAN TGM 15.290 vehicle that went to take part in emergency action in buildings situated approximately 500 m away from paved roads. The developed methodology may be widely used in crisis management, where the emergency actions often require rescue units to access remote places.

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.

The study aims at land cover prediction based on cellular automata and artificial neural network (CA-ANN) method implemented in the Methods Of Land Use Change Evaluation (MOLUSCE) tool. The Tricity region and the neighbouring counties of Gdański and Kartuzy were taken as the research areas, and coordination of information on the environment (CORINE Land Cover, CLC, CLMS 2022) data for 2006, 2012 and 2018 were used to analyse, simulate and predict land cover for 2024, the next reference year of the CORINE inventory. The results revealed an increase in artificial surfaces, with the highest value during the period 2006–2012 (86.56 km²). In total, during the period 2006–2018, the growth in urbanised area amounted to 95.37 km². The 2024 prediction showed that artificial surfaces increased by 9.19 km², resulting in a decline in agricultural land.