The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time.

This research is primarily focused on utilizing available airborne LiDAR data and spatial data from the OpenStreetMap (OSM) database to generate 3D models of buildings for a large-scale urban area. The city center of Ljubljana, Slovenia, was selected for the study area due to data availability and diversity of building shapes, heights, and functions, which presented a challenge for the automated generation of 3D models. To extract building heights, a range of data sources were utilized, including OSM attribute data, as well as georeferenced and classified point clouds and a digital elevation model (DEM) obtained from openly available LiDAR survey data of the Slovenian Environment Agency. A digital surface model (DSM) and digital terrain model (DTM) were derived from the processed LiDAR data. Building outlines and attributes were extracted from OSM and processed using QGIS. Spatial coverage of OSM data for buildings in the study area is excellent, whereas only 18% have attributes describing external appearance of the building and 6% describing roof type. LASTools software (rapidlasso GmbH, Friedrichshafener Straße 1, 82205 Gilching, GERMANY) was used to derive and assign building heights from 3D coordinates of the segmented point clouds. Various software options for procedural modeling were compared and Blender was selected due to the ability to process OSM data, availability of documentation, and low computing requirements. Using procedural modeling, a 3D model with level of detail (LOD) 1 was created fully automated. After analyzing roof types, a 3D model with LOD2 was created fully automated for 87.64% of buildings. For the remaining buildings, a comparison of procedural roof modeling and manual roof editing was performed. Finally, a visual comparison between the resulting 3D model and Google Earth’s model was performed. The main objective of this study is to demonstrate the efficient modeling process using open data and free software and resulting in an enhanced accuracy of the 3D building models compared to previous LOD2 iterations.

The project is aimed at upgrading existing and outdated maps from the time when the present-day area of the Diocese of Požega in Croatia was under the Archdiocese of Zagreb. We could not find a single map of the Diocese of Požega since its establishment in 1997. That is why in 2001 we began to create one at the Faculty of Geodesy. It has been developed in stages and this work is still ongoing. The paper describes how the new map was created, it explains its purpose and the motivation behind the idea to create the first map of this diocese, and mentions obstacles that had to be dealt with during the research of certain specific thematic content. It also describes several working versions of the map. The project on which the paper is based is unique because it investigates sacral topics and facilities which have not yet been identified or depicted cartographically. The authors have successfully created a map which is built around sacral themes and emphasized cartographic details relating to this diocese. The procedure, though complicated due to the lack of available data, entailed collection and analysis of commonplace data and depicts authors’ exposure to the difficulties of making and designing map.

Mines remaining from the Homeland War are a huge problem in Croatia. The joint work of experts in humanitarian demining and military doctrines in certain geographical areas, and scientists of various profiles, has resulted in a concept for producing mine danger maps, which show areas and levels of potential hazards from mines, i.e. suspected hazardous areas. This paper presents the concept for producing mine danger maps for a suspected hazardous area (Svilaja, Croatia). The input data comprise information stored in mine information systems and additional data collected on the suspected hazardous area (e.g. bunkers and shelters for tanks, artillery and people). The resulting maps (Main Map) seek to improve the identification of areas where there is no threat so that parts of suspected hazardous areas can be proposed for mine reduction, or suspected hazardous areas can be better defined.