Search Results (444)

Geospatial tools and geocoding systems play an increasingly significant role in the modernization and operation of municipal water utility networks. This research explored how geocoding systems could improve network management, facilitate leak detection, and enhance hydraulic modeling accuracy. Various geocoding services, including Google, Bing Maps, and OpenStreetMap APIs were analyzed using address data from a small Central European municipality. The analysis was performed in February and March of 2024. The accuracy and efficiency of these systems in handling spatial data for domestic water networks were assessed and results showed that geocoding accuracy depended on the quality of the service provider databases and the formatting of input data. Google proved the most reliable, while Bing and OpenStreetMap were less accurate. Additionally, the Location Database developed by Lechner Knowledge Center was used as a reliable local reference for comparison with global services. Geocoding results were integrated into GIS softwares (Google Earth ver. 7.3.6.9796, QGIS ver. 3.36, ArcGIS ver 10.8.2) to enable spatial analysis and comparison of geographic coordinates. The findings highlight geocoding’s critical role in efficient water network management, particularly for mapping consumer data and rapidly localizing leaks and breaks. Our findings directly support hydraulic modeling tasks, contributing to sustainable operations and cost-effective interventions. Full article

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

Green open space in high-density cities has positive significance in terms of improving the quality of the living environment and solving problems such as “urban diseases”. Taking the high-density urban districts of the Macau Peninsula and Monaco as examples, this study divides the planning index of open space green quantity into two dimensions: the blue-green spaces occupancy rate (BGOR) within urban land areas and the blue-green spaces visibility rate (BGVR) of the main streetscape. Using satellite remote-sensing maps, GIS databases, and street-view images, this study evaluates the current green quantity in both regions and compares them to identify best practices. This study aims to assess and enhance the green quantity found in the open spaces of high-density cities, using the Macau Peninsula and Monaco as case studies. The primary research questions are as follows: (1) How can the green quantity in open spaces be effectively measured in high-density urban environments? (2) What planning strategies can be implemented to increase the green quantity and improve the urban living environment in such areas? Therefore, this study proposes planning strategies such as three-dimensional greening, converting grey spaces to green spaces, and implementing policies to encourage public participation in greening efforts. These strategies aim to enhance the green quantity in open spaces, thereby improving the urban living environment in high-density cities like Macau and providing a reference for similar urban areas in the world. Full article

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. Full article

This paper explores the innovative application of Geographic Information Systems (GISs) to identify and utilize waste heat sources from railway and highway tunnels for fifth-generation district heating and cooling (5GDHC) systems. Increasing the number of prosumers—entities that produce and consume energy—within 5GDHC networks enhances their efficiency and sustainability. While potential sources of waste heat vary widely, this study focuses on underground car/railway tunnels, which typically have a temperature range of 20 °C to 40 °C. Using GIS software, we comprehensively analyzed tunnel locations and their potential as heat sources in Belgium. This study incorporates data from various sources, including OpenStreetMap and the European Waste Heat Map, and applies a two-dimensional heat transfer model to estimate the heat recovery potential. The results indicate that railway tunnels, especially in the southern regions of Belgium, show significant promise for waste heat recovery, potentially contributing between 0.8 and 2.9 GWh annually. The integration of blockchain technology for peer-to-peer energy exchange within 5GDHC systems is also discussed, highlighting its potential to enhance energy management and billing. This research contributes to the growing body of knowledge on sustainable energy systems and presents a novel approach to leveraging existing district heating and cooling infrastructure. Full article

Presenting real-world paths in property graphs is a complex challenge of identifying and representing the properties of routes and their environments. These property graphs serve as foundational datasets for generating smart sports training routes, where route features such as terrain, bends, and hills critically influence the route design. This paper outlines a method for identifying key parameters of real-world paths and encoding them into property graphs. The proposed method has significant implications for sports event planning, particularly in designing route-based training that meets specific athletic challenges. The research concludes by presenting a case study in which a property graph that enables cycling route generation was created for the country of Slovenia, and a sample training route was generated. Full article

The evolving capabilities of Geographic Information Systems (GISs) are transforming various industries, including e-commerce, by providing enhanced spatial analysis and precision in customer targeting, and improving the ability of recommender systems. This paper proposes a novel framework that integrates mobile agents with GISs to deliver real-time, personalized recommendations in e-commerce. By utilizing the OpenStreetMap API for geographic mapping and the Java Agent Development Environment (JADE) platform for mobile agents, the system leverages both geospatial data and customer preferences to offer highly relevant product suggestions based on location and behaviour. Mobile agents enable real-time data collection, processing, and interaction with customers, facilitating dynamic adaptations to their needs. The combination of GISs and mobile agents enhances the system’s ability to analyze spatial data, providing tailored recommendations that align with user preferences and geographic context. This integrated approach not only improves the online shopping experience but also introduces new opportunities for location-specific marketing strategies, boosting the effectiveness of targeted advertising. The validation of this system highlights its potential to significantly enhance customer engagement and satisfaction through context-aware recommendations. The integration of GISs and mobile agents lays a strong foundation for future advancements in personalized e-commerce solutions, offering a scalable model for businesses looking to optimize marketing efforts and customer experiences. Full article

Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several challenges. They often rely on costly commercial platforms, struggle with integrating heterogeneous datasets, and require complex workflows to achieve both high precision and comprehensive campus coverage. This paper addresses these issues by proposing a systematic multi-source data fusion approach that employs open-source technologies to generate a real-scene 3D digital campus. A case study of Lanzhou Jiaotong University is presented to demonstrate the feasibility of this approach. Firstly, oblique photography based on unmanned aerial vehicles (UAVs) is used to capture large-scale, high-resolution images of the campus area, which are then processed using open-source software to generate an initial 3D model. Afterward, a high-resolution model of the campus buildings is then created by integrating the UAV data, while 3D Digital Elevation Model (DEM) and OpenStreetMap (OSM) building data provide a 3D overview of the surrounding campus area, resulting in a comprehensive 3D model for a real-scene digital campus. Finally, the 3D model is visualized on the web using Cesium, which enables functionalities such as real-time data loading, perspective switching, and spatial data querying. Results indicate that the proposed approach can effectively get rid of reliance on expensive proprietary systems, while rapidly and accurately reconstructing a real-scene digital campus. This framework not only streamlines data harmonization but also offers an open-source, practical, cost-effective solution for real-scene 3D digital campus construction, promoting further research and applications in twin city, Virtual Reality (VR), and Geographic Information Systems (GIS). Full article

Although there are many articles dealing with web map applications, they often focus on just one or a few applications. Several articles deal with the technical solution of the applications, but relatively few are focused on the cartographic aspects of these applications. This article evaluates eight web mapping applications based on six cartographic aspects: map key, map scale, map layout, navigation elements, labels, and analytical tools. The objective is to identify differences in the presentation of geographic information and propose improvements for cartographic quality and user-friendliness. The methodology involved visual analysis at two scales. The comparison included applications such as Mapy.cz, OpenStreetMap, Google Maps, Bing Maps, HERE Maps, MapQuest, ViaMichelin, and Locus Map. The results revealed significant differences among the applications that may impact user orientation and experience. For instance, Google Maps does not display forest symbols on its default map, which can reduce clarity, whereas Mapy.cz offers the most comprehensive range of analytical tools. Advertisements in applications like MapQuest and ViaMichelin disrupt the user experience, and some applications lack essential functions, such as distance measurement. The paper identifies strengths and weaknesses in the cartographic design of these applications. Findings reveal that while each application possesses unique characteristics, they share common features. An interesting feature is the absence of cartographic symbols and labels of some elements in some applications. The study recommends the unification of cartographic principles and further user testing to optimize the layout and functionality of web mapping applications. Full article

Cross-view geo-localization (CVGL) aims to determine the capture location of street-view images by matching them with corresponding 2D maps, such as satellite imagery. While recent bird’s eye view (BEV)-based methods have advanced this task by addressing viewpoint and appearance differences, the existing approaches typically rely solely on either OpenStreetMap (OSM) data or satellite imagery, limiting localization robustness due to single-modality constraints. This paper presents a novel CVGL method that fuses OSM data with satellite imagery, leveraging their complementary strengths to enhance localization robustness. We integrate the semantic richness and structural information from OSM with the high-resolution visual details of satellite imagery, creating a unified 2D geospatial representation. Additionally, we employ a transformer-based BEV perception module that utilizes attention mechanisms to construct fine-grained BEV features from street-view images for matching with fused map features. Compared to state-of-the-art methods that utilize only OSM data, our approach achieves substantial improvements, with 12.05% and 12.06% recall enhancements on the KITTI benchmark for lateral and longitudinal localization within a 1-m error, respectively. Full article

City services and infrastructures are focused on consumers and are able to effectively and qualitatively implement their functions only under conditions of normal workload. In this regard, the correct organization of a public service system is directly related to the knowledge of the quantitative and qualitative composition of people in the city during the day. The article discusses existing solutions for analyzing the distribution of people in a territory based on data collected by mobile operators, payment terminals, navigation systems and other network solutions, as well as the modeling methods derived from them. The scientific aim of the study is to propose a solution for modeling the daily distribution of people based on open statistics collected from the Internet and open-web mapping data. The stages of development of the modeling software environment and the methods for spatial analysis of available data on a digital cartographic basis are described. The proposed approach includes the use of archetypes of social groups, occupational statistics, gender and age composition of a certain territory, as well as the characteristics of urban infrastructure objects in terms of composition and purpose. Solutions for modeling the 48 h distribution of city residents with reference to certain infrastructure facilities (residential, public and working) during working and weekend days with an hourly breakdown of the simulated values were created as a result of the study. A simulation of the daily distribution of people in the city was carried out using the example of the city of Volgograd, Russian Federation. A picture of the daily distribution of city residents by district and specific buildings of the city was obtained as a result of the modeling. The proposed approach and the created algorithm can be applied to any city. Full article

This article analyzes the service distribution imbalance within the Metropolitan District of Quito (DMQ) and its impact on urban mobility, aiming to propose strategies for more equitable territorial planning. The data were gathered from sources such as the National Institute of Statistics and Census (INEC), the Ministry of Health, the Ministry of Education, and OpenStreetMap. These data were integrated with GIS tools to model patterns of accessibility and mobility. Through a comprehensive approach, the study assessed education, banking services, employment, and healthcare, identifying how inequitable access to these services drives increased travel demand, especially in rural and peri-urban areas. In the education field, over 500 neighborhoods faced a shortage of institutions, compelling students to commute to other neighborhoods. For financial services, only 67% of neighborhoods had adequate access, with disparities across different socioeconomic zones. Additionally, employment-related mobility posed another challenge, with 88% of workers commuting outside their residential parish. Finally, access to healthcare was also unequal across the DMQ, particularly in peripheral areas where residents must travel long distances. In this context, it can be concluded that more efficient urban planning in the Metropolitan District of Quito (DMQ) is crucial to address imbalances in the distribution of services and enhance quality of life. Proposed strategies include establishing a land reserve, decentralizing services to underserved areas, integrating smart technologies, and promoting incentives for remote work, sustainable mobility, and public transport. These actions aim to foster greater territorial equity and accessibility. Full article

The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating measure values of connected road features, thereby integrating extensive contextual information from the network hierarchy. In our evaluation, conducted across diverse urban landscapes in six cities in Italy and Türkiye, we tested two geometry-, six centrality-, and eight semantic-based features to predict road functional classes stored as a highway = * key in OSM. The findings indicate a marginal impact of geometric features and city identifiers on classification performance. Utilizing centrality attributes alongside semantic features in a direct, non-hierarchical manner results in an F1 score of 80%. However, integrating these features in our network-based hierarchical feature augmentation process remarkably increases the F1 score up to 85%. The success of our approach underlines the importance of network-based feature engineering in capturing the complex dependencies of geographic data, considering a more accurate and contextually aware OSM classification framework. Full article

This work presents the mathematical definition and programming considerations of an efficient geometric algorithm used to add roofs to polyhedral 3D building models obtained from OpenStreetMap. The algorithm covers numerous roof shapes, including some well-defined shapes that lack an explicit reconstruction theory. These shapes include gabled, hipped, pyramidal, skillion, half-hipped, gambrel, and mansard. The input data for the developed code consist of latitude and longitude coordinates defining the target area. Geospatial data necessary for the algorithm are obtained through a request to the overpass-turbo service. The findings showcase outstanding performance for buildings with straightforward footprints, but they have limitations for the ones with intricate footprints. In future work, further refinement is necessary to solve the mentioned limitation. Full article

Among the most significant impacts related to the spread of settlements and the densification of urban areas, the reduction in the availability of public green spaces plays a central role in the definition of livable cities, in terms of the environment and social cohesion, interaction, and equality. In the framework of target 11.7 of the Sustainable Development Goals (SDG) 11, the United Nations has established the objective of ensuring universal, safe, and inclusive access to public spaces by 2030, for women, children, the elderly, and people with disabilities. This study proposes the evaluation of this objective for the urban area of the 14 Italian metropolitan cities, as defined by EUROSTAT and adopted by the United Nations and the Nature Restoration Law (NRL). A methodology based on open-source data and network analysis tools is tested for the provision of an unprecedented mapping of the availability and accessibility to green urban public spaces, which shows that less than 30% of metropolitan city residents have access to a green space within 300 m on foot, according to OpenStreetMap data (less than one in five for the Urban Atlas data). Furthermore, a critical analysis on the geometric and semantic definition of green urban public spaces adopted by the main European and international tools is carried out, which underlines the strategic role of crowdsourcing but also the need for mapping rules that make the data more consistent with the monitoring objectives set at the institutional level. Full article