The cities of the future should not only be smart, but also smart green, for the well-being of their inhabitants, the biodiversity of their ecosystems and for greater resilience to climate change. In a smart green city, the location of urban green spaces should be based on an analysis of the ecosystem services they provide. Therefore, it is necessary to develop appropriate information technology tools that process data from different sources to support the decision-making process by analysing ecosystem services. This article presents the methodology used to develop an urban green space planning tool, including its main challenges and solutions. Based on the integration of data from ALS, CLMS, topographic data, and orthoimagery, an urban green cover model and a 3D tree model were generated to complement a smart-city model with comprehensive statistics. The applied computational algorithms allow for reports on canopy volume, CO${}_2$ reduction, air pollutants, the effect of greenery on average temperature, interception, precipitation absorption, and changes in biomass. Furthermore, the tool can be used to analyse potential opportunities to modify the location of urban green spaces and their impact on ecosystem services. It can also assist urban planners in their decision-making process.

In this paper, the regionalization of geographical space according to selected topographic factors and the spatial distribution of precipitation is discussed. The model takes into account qualitative and quantitative data describing the conditions associated with the studied precipitation. In the modelling, data mining methods including data clustering methods for agglomeration and artificial neural networks for classification have been used. The reason for their use was the classification of the area due to conditions related to precipitation, the distinguishing of similar areas and the delimitation of the propagation of the phenomenon or transition zones. To realize the research aims, professional software for data management, spatial data analysis, mathematical calculations and data mining have been used. The result of the research was a model of the classes representing areas with specific conditions affecting the phenomenon, transition zones between classes and areas with conditions other than those in the surroundings of the measuring stations, which are not classified in any of the classes. Classification results indicate the boundaries of the areas in which we can model the values measured at stations, the transition zones of possible discontinuous change and areas in which the phenomenon should not be modelled due to significantly different conditions from those in the neighbourhoods of measuring stations. Unclassified areas are also potential locations for new measuring stations.

This paper identifies the threats and risks of a terrorist attack on a critical infrastructure facility based on the example of Żelazny Most Tailings Storage Facility (OUOW). The threat analysis primarily took into account the threats of deliberate human actions. Identification of potential threats concerning the infrastructure surrounding the facility was conducted based on information that is readily available on the Internet. The reasons why it may be a potential target were also justified. Numerical calculations of the stress–deformation scale of the initial state of the reservoir, based on the Biot model with the Kelvin–Voight rheological skeleton, were presented as a starting point for in-depth research on the scale of threats and risks to the reservoir. The presented numerical model can be a starting point for calculating the stability of a reservoir subjected to explosives. The facility constitutes a major element of Lubińsko-Głogowski Okręg Miedziowy (Lubin-Głogów Copper District). OUOW Żelazny Most is the biggest such facility in Europe and is utilized to collect tailing waist. When expanded in its southern quarter, the facility will be the biggest in the world.

This paper undertakes the problem of mapping a hyperboloid cooling tower on a single plane. Measurements performed by ground-based laser scanning technology quickly deliver substantial amounts of geometric data of the tower’s outer wall. The essence of the article is projection of the rotational hyperboloid on a plane. The shape of the hyperboloid cooling tower is not directly expandable to a single plane. Mapping a hyperboloid shape on a plane is, therefore, associated with distortions. This paper presents a comparison between cylindrical and conic projection of a hyperboloid cooling tower. The most popular method of mapping hyperboloid is cylindrical projection. The cylinder’s side surface is easily developed on the drawing sheet. For the hyperboloid cooling tower, the biggest distortions occur in the latitudinal direction and reach the highest values at the top and bottom edges. The equation (13) describe distortion for the cylindrical projection. The equation (18) describe distortion for the conical projection. This paper presents results obtained from the performed measurement. The analysis found that cone mapping produces less distortion than cylindrical projection for the hyperboloid cooling tower. We think, that in conical projection, the shape of a hyperboloid cooling tower and theoretical conic shape have better corresponding together than in cylindrical projection.

The development of generalisation (simplification) methods for the geometry of features in digital cartography in most cases involves the improvement of existing algorithms without their validation with respect to the similarity of feature geometry before and after the process. It also consists of the assessment of results from the algorithms, i.e., characteristics that are indispensable for automatic generalisation. The preparation of a fully automatic generalisation for spatial data requires certain standards, as well as unique and verifiable algorithms for particular groups of features. This enables cartographers to draw features from these databases to be used directly on the maps. As a result, collected data and their generalised unique counterparts at various scales should constitute standardised sets, as well as their updating procedures. This paper proposes a solution which consists in contractive self-mapping (contractor for scale s = 1) that fulfils the assumptions of the Banach fixed-point theorem. The method of generalisation of feature geometry that uses the contractive self-mapping approach is well justified due to the fact that a single update of source data can be applied to all scales simultaneously. Feature data at every scale s < 1 are generalised through contractive mapping, which leads to a unique solution. Further generalisation of the feature is carried out on larger scale spatial data (not necessarily source data), which reduces the time and cost of the new elaboration. The main part of this article is the theoretical presentation of objectifying the complex process of the generalisation of the geometry of a feature. The use of the inherent characteristics of metric spaces, narrowing mappings, Lipschitz and Cauchy conditions, Salishchev measures, and Banach theorems ensure the uniqueness of the generalisation process. Their application to generalisation makes this process objective, as it ensures that there is a single solution for portraying the generalised features at each scale. The present study is dedicated to researchers concerned with the theory of cartography.

This study presents an integrated workflow for acquiring, processing, and fusing terrestrial laser scanning and Unmanned Aerial Vehicle (UAV) photogrammetric data to generate digital twins of heritage buildings within Heritage Building Information Modeling (HBIM) and Historical Geographic Information System (HGIS) environments. Using a historic wooden church as a case study, the proposed approach demonstrates improved completeness and geometric quality compared to UAV-only models. Dimensional differences between UAV-only and integrated models ranged from 0.8 to 3.2 cm, confirming internal consistency and suitability for documentation purposes. The workflow standardizes key stages of acquisition, scaling, and point cloud fusion, and establishes links between HBIM models at Level of Detail (LOD) 100–300 and conservation requirements. Additionally, it identifies integration points for Artificial Intelligence (AI)-based automation, supporting future developments in classification, segmentation, and conversion of 2D documentation into HBIM. The results highlight the potential of terrestrial laser scanning (TLS)-UAV integration for accurate, replicable heritage documentation and spatial–historical analysis.