The sustainability of cultural heritage is one of the grand challenges of the contemporary world. Part of preservation is long-term monitoring, and LiDAR techniques are exceptionally useful. Despite all its advantages LiDAR is still not perfect, as individual points do not have a specific colour. This paper outlines a method for applying color to a LiDAR point cloud of a wall painting using images, leveraging the ICP algorithm. The results demonstrate that the proposed methodology is effective for applying colour to the LiDAR point cloud of wall paintings.

The optimal composition of charge is a major problem in the Metallurgical industry. It is necessary to select packages of raw material from a stock, and to assemble the appropriate combination of elements to meet the criteria of a particular alloy. We approached the optimisation problem with a genetic algorithm. As the results show, the genetic algorithm is a suitable procedure for the optimisation problem of selecting the correct combination of packages to assemble the charge.

The book Preteklost in prihodnost ('The past and the future') is the sixteenth volume in the GIS v Sloveniji (GIS in Slovenia) book series and commemorates its 30th anniversary. The goal of the volume is to present the wide variety of research findings on geographical information systems in Slovenia in recent years. Powerful geoinformatic tools and precise data facilitate research on processes and phenomena, and their modelling. The volume presents project outputs and research results in areas such as geology, geomorphology, hydrology, pedology, agriculture, natural disasters, environmental protection, geography, surveying, archaeology, transport, telecommunication infrastructure, tourism, cultural heritage, education, cartography, geographical names, remote sensing, and others. Readers discover new features regarding the applicability of geographical information systems and learn about interesting research findings in many areas.

Collecting voluntary photographs of topographic changes and the analysis of their usefulnessThis paper presents three controlled campaigns for collecting volunteer photographs of topographic changes.We collected photographs of spatial changes that may be useful for the maintenance of the 1:5000 scale topographic map or the data of the so-called national topographic model.A total of 195 potential volunteers took part in those three volunteered geographic information (VGI) campaigns: all employees of the Geodetic Institute of Slovenia, part of the employees of UM FERI, two classes of students on UM FERI and

The pandemic caused by the coronavirus COVID-19 is having a worldwide impact that affects health, economy and air pollution in cities indirectly. In Slovenia, as well as in all other countries, the number of cases of infected people increased continually in 2020, which affected the health system and caused movement restrictions, which, in turn, affected the air pollution in the country. This article presents the indirect effect produced by this pandemic on air pollution in Maribor, Slovenia. Traffic and air quality data were used to perform the evaluation, in particular PM10 and PM2.5 daily concentrations from the monitoring station in Maribor. By observing the de-tailed traffic data and particulate matter concentrations acquired in the Maribor city centre before and during the pandemic times, we show the influence of COVID-19 on particulate matter concentrations in that part of the town. The results show slightly lower particulate matter con-centrations, which could be explained by the significantly lower traffic volume values in the lockdown months.

Acts of fraud have become much more prevalent in the financial industry with the rise of technology and the continued economic growth in modern society. Fraudsters are evolving their approaches continuously to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. To overcome and investigate financial frauds, this paper presents STALITA, which is an innovative platform for the analysis of bank transactions. STALITA enables graph-based data analysis using a powerful Neo4j graph database and the Cypher query language. Additionally, a diversity of other supporting tools, such as support for heterogeneous data sources, force-based graph visualisation, pivot tables, and time charts, enable in-depth investigation of the available data. In the Results section, we present the usability of the platform through real-world case scenarios.

The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents a dedicated space component of the European Copernicus Programme, committed to long-term operational services in the environment, climate and security. A huge amount of obtained data allows us different surveys. We decided to detect changes in the snow cover in the Julian Alps at the different seasons. The differences have been calculated using Sentinel-1 images from each season period. The presented methodology consist of five main steps, where the most important step is the calculation of Differential SAR Interferometry (DInSAR). By doing this, we found out how the thickness of the snow changes during the seasons. As demonstrated by the results, the presented approach is suitable for detection of snow level changes.

PM10 particles impose significant risks to human health and the well-being of individuals in general. However, due to the complexity of the inner-correlations between influencing environmental factors, the holistic approach to predictive analytics of PM10 concentration levels is a challenging task yet to be undertaken. We base this study on the rationale that a prediction model is suitable for making accurate estimations involving knowledge about the hidden interactions that govern them. In addition to the model's precision, it is, therefore, beneficial to provide a model that is interpretable, as this can assist in the decision about how and which prevention actions to take. For this purpose, a Genetic Algorithm is proposed that carries out multiple regression analysis by searching for the optimal fictional definition of a prediction model. As such, the obtained model is human interpretable, where the preliminary analysis conducted within this study proved its compliance with the existing studies, while the model itself proved to be considerably more accurate than the present state-of-the-art.

Competition between individual trees is a major factor influencing the development of forests. However, due to the complexity of such interactions, that span over vast geographic areas, systematic analysis of competition has only recently become possible through the concepts of so-called predictive analytics. The rationale behind the utilised approach is that a prediction model, which is capable of forecasting future increments of tree development parameters accurately, contains knowledge about the underlying relationships that govern them. The analysis of such model, therefore, holds the potential to reveal new insights into the critical factors that influence forest developments. Within this study, we utilise an Evolutionary Algorithm in order to enable predictive analytics based on a complex-network representation of competition. This allowed us to study the patterns related to spatial distribution of individual trees. We discovered that triplets of competing trees, and their betweenness centralities, have significantly greater influence on the development of each individual tree than traditionally observed parameters like the number of a tree's competitors and distances between them. While this indicates preferable spatial patterns for optimal forest development, the introduced methodology proved to be an efficient predictive analytics tool that allows for their discovery.

This paper proposes a novel visualization and analytics tool, which is capable of searching for hidden relationships and patterns within large multi-dimensional data. The goal of the presented tool is to represent the data in novel ways, understandable and useful to the data owner, with new visual and statistical analytics. Various statistics are offered to the user in order to search for linear and nonlinear correlations between multiple variables. Using a simple dataset, we confirmed the suitability of the proposed tool for revealing new relationships and patterns in the used multi-dimensional data.

Complex network theory offers an efficient mathematical framework for modelling natural phenomena. However, these studies focus mainly on the topological characteristics of networks, while the actual reasons behind the networks’ formation remain overlooked. This paper proposes a new approach to complex network analysis. By searching for the optimal functional definition of the network's edge set, it allows an examination of the influences of the physical properties of the nodes on the network's structure and behaviour (i.e. changes of the network's structure when the physical properties of nodes change). A two-level evolutionary algorithm is proposed for this purpose, whereby the search for a suitable function form is achieved at the first level, while the second level is used for optimal function fitting. In this way, not only the features with the largest influences are identified, but also the intensities of their influences are estimated. Synthetic networks are examined in order to show the superiority of the proposed approach over traditional machine learning algorithms, while the applicability of the proposed method is demonstrated on a real-world study of the behaviour of biological cells.