Search Results (147)

This study explores the potential of ChatGPT-4, an AI-powered large language model, to generate thematic maps and compare its outputs to the traditional method in which maps are produced manually by humans using GIS software. Prompt engineering is a crucial methodology of large language models that can enhance output quality. The main objective of this study is to assess the capability of AI-generated maps and to compare the quality with a traditional method. The study evaluates two prompt patterns: basic (zero-shot prompts) and advanced (Cognitive Verifier and Question Refinement). The performance of AI-generated maps is assessed based on attempts, errors, incorrect results, and map completeness. The final stage involved evaluating AI-generated maps against cartographic rules to assess their suitability. ChatGPT-4 performs well in generating suitable choropleth maps but faced challenges in understanding the prompts and potential errors in the generated code. Advanced prompts reduced errors and improved the quality of outputs, particularly for complex map elements. This paper enhances the understanding of AI’s role in cartography and further research in automated cartography. The study assesses cartographic aspects, offering insights into the strengths and limitations of AI in cartography, illustrating how large language models can process geospatial data and adhere to cartographic principles. The study also paves the way for future innovations in automated geovisualization. Full article

In response to the increasing demand for effective bridge management and the shortcomings of current proprietary solutions, this work presents an open-source, web-based platform designed to support bridge inspection and data management, particularly for small and medium-sized public administrations, which often lack personnel or funding for implementing context-specific tools. The system addresses fragmented workflows by integrating multi-format geospatial and 3D data—such as point clouds, CAD/BIM models, and georeferenced imagery—within a unified, modular architecture. The platform enables structured inventory, interactive 2D/3D visualization, defect annotation, and role-based user interaction, aligning with FAIR principles and interoperability standards. Built entirely with free and open-source tools, the P.O.N.T.I. prototype ensures scalability, transparency, and adaptability. A multi-layer navigation interface guides users through asset exploration, inspection history, and immersive 3D viewers. Fully documented and publicly available on GitHub, the system allows for deployment across varying institutional contexts. The platform’s design anticipates future developments, including integration with IoT monitoring systems, AI-driven inspection tools, and chatbot interfaces for natural language querying. By overcoming existing proprietary limitations and providing access to a versatile single space, the proposed solution supports decision-makers in the digital transition towards a more accessible, transparent and integrated infrastructure asset management. Full article

As road users and means of transport in Germany become more diverse, we must better understand the causes and influencing factors of serious crashes. The aim of this work is to develop an AI-supported analysis approach that identifies and clearly visualizes the causes of crashes and their impact on crash severity in the urban area of the city of Mainz. The machine learning models predict crash severity and use Shapley values as explainability methods to make the underlying patterns understandable for urban planners, safety personnel, and other stakeholders. A particular challenge lies in presenting these complex relationships in a user-friendly way through visualizations and interactive maps. Full article

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, COD, Na⁺). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSE_WQI = 0.1205, RMSE_{CCME WQI} = 0.1305) and the highest determination coefficient (R²_WQI = 0.9916, R²_{CCME WQI} = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R² = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns. Full article

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance in visual search tasks through two eye-tracking experiments. Experiment 1 investigated the effects of two visual coding factors: encoding types (flashing, pulsation, and lightness modulation) and animation rates (low, medium, and high). Experiment 2 focused on the interaction between encoding types and modulation areas (fill, contour, and entire symbol) under a fixed animation rate condition. The results revealed that search performance deteriorates as the animation rate of the fastest target symbol exceeds 10 fps. Flashing and lightness modulation outperformed pulsation, and modulation areas significantly impacted efficiency and accuracy, with notable interaction effects. Based on the experimental results, three visual coding strategies are recommended for optimal performance in map-based interfaces: contour pulsation, contour flashing, and entire symbol lightness modulation. These findings provide valuable insights for optimizing the design of dynamic point symbols, contributing to improved user engagement and task performance in cartographic and geovisual applications. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

As the diversity and complexity of geographic statistical data continue to increase, it becomes increasingly important to present multi-level information in order to meet a broader range of needs. In response to the limitations of existing visualization methods in representing the geographic distribution of statistical data, this paper proposes a geographical hierarchical data visualization method based on rectangular cartograms. First, a new rectangular cartograms construction algorithm is adopted in this paper, which can effectively preserve relatively accurate orientation and adjacency relationships between geographic regions, while also effectively preserving the statistical data features. Then, a treemap layout algorithm is applied within the rectangular cartogram to further partition the geographic regions, thereby visualizing the hierarchical structure of the data. Through experimental validation using real datasets and usability testing, the results demonstrate that the method presented in this paper excels in geographic distribution representation, hierarchical relationship visualization, and information readability. Compared to traditional thematic map methods, this approach demonstrates significant advantages in terms of information transmission efficiency and shows promising performance in expressive effectiveness, providing strong support for the analysis and decision making of geographical hierarchical data. Full article

Images of melting ice have become powerful symbols of climate change, attracting both public attention and scientific interest. This research uses web technologies to document and communicate the ongoing retreat of the Belvedere Glacier in the Italian Alps. By combining historical and contemporary 2D and 3D geospatial data, the paper presents a comprehensive digital platform that allows visualization of long-term changes of the Belvedere Glacier. To increase public understanding and engagement, we develop a user-friendly web platform that provides interactive tools for exploring glacier data. By fostering a deeper understanding of the complex processes involved in glacier retreat by different audiences (students, general public, and technical experts), this work aims to inspire further research and cooperation, also thanks to the reproducibility of the open-source code. Full article

The United States identifies, monitors, and defends a vast network of controlled airspaces surrounding its own and allied territories. These controlled airspaces include civilian aviation classes (A through G), drone flying regions, and special use (military) air classifications. These controlled spaces are invisible to the naked eye and often go unnoticed. Managing and portraying data that function in two and three dimensions poses significant challenges that have hindered prior analyses or geovisualizations of controlled airspaces, but we demonstrate here how many of these can be surmounted to visually represent the spatial extent and patterns of US-controlled airspace. In this paper, we demonstrate how these complex spaces can be graphically represented and highlight how cartographic and geovisual representations of often-overlooked domains contribute to a richer understanding of the reach and character of US airspace. The methods described for this work can be extended to other types of multidimensional objects and may facilitate more robust considerations of how Geographical Information Science (GIS) can be useful in analyzing and depicting airspace and territorial claims in three dimensions. Full article

The rectangular cartogram is a geospatial visualization method that blends the characteristics of maps and charts. By simplifying geographic regions into rectangles and using the area of each rectangle to represent statistical data, it enables efficient geovisualization. This paper summarizes and analyzes the advantages and limitations of two main approaches used in current rectangular cartogram construction algorithms. To address the issues of high computational cost and inadequate preservation of adjacency and relative positional relationships in existing algorithms, we propose and implement a new rectangular cartogram construction algorithm. This algorithm simplifies the layout computation process while ensuring that the adjacency and relative positional relationships between regions during the layout generation process have only minor errors. In adjusting rectangle areas to match attribute values, the algorithm adopts a “region-by-region placement” strategy, ensuring that errors in area accuracy remain within a small range, while also keeping errors in adjacency and relative positional relationships minimal. Finally, by comparing the results of our algorithm with those of existing algorithms using real-world data with varying distribution characteristics, we demonstrate its effectiveness. The results show that the proposed algorithm not only improves computational efficiency but also effectively displays the adjacency and relative positional relationships between regions. Full article

The North Java coastal area, known as the Pantura region, is experiencing significant land subsidence, with certain areas sinking up to 10 cm per year. Pekalongan is among the most affected, with subsidence rates between 10 and 19 cm annually, mainly due to groundwater extraction, sediment compaction, and coastal erosion. Other coastal cities, like Semarang and Demak, show rates averaging 4 to 10 cm per year. This rapid subsidence is due to favorable geological conditions and ongoing urban development. This study investigates land subsidence in Pekalongan using the PS-InSAR method and dynamic visualization of time-series land cover data. PS-InSAR was applied to 45 scenes from ALOS-2 PALSAR-2 to monitor subsidence from 2014 to 2022. The results were validated with in situ subsidence benchmarks. Urban development dynamics were analyzed through land cover and land use change (LULC) and population density over the same period, using the GLC_FCS30D dataset in the GEE to detect non-natural LULC. The PS-InSAR results indicated that over 60.9% of investigation points experienced subsidence, up to 100 cm between 2014 and 2022. Ground validation showed an 83% agreement with PS-InSAR results. A statistical analysis of LULC from 2014 to 2022 did not show significant built-up area development, but the extension of salt marshes and water bodies indicated subsidence expansion. The population density reached 6873 people per square km by 2022, causing extensive groundwater use for domestic and industrial purposes, further aggravating the subsidence. Full article

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map, these characteristics can lead to point clutter, reducing the readability of the map product and misleading users to false interpretations of patterns in the data, e.g., regarding specific clusters or extreme values. With this work, we provide a framework that is able to generalize point data, preserving spatial clusters and extreme values simultaneously. The framework consists of an agent-based generalization model using predefined constraints and measures. We present the architecture of the model and compare the results with methods focusing on extreme value preservation as well as clutter reduction. As a result, we can state that our agent-based model is able to preserve elementary characteristics of point datasets, such as the point density of clusters, while also retaining the existing extreme values in the data. Full article

This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km² and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems since they present different challenges and require different approaches. This article presents several relevant scientific studies and projects that have successfully used game engines for similar purposes. This case study focuses on the computational techniques used in Unreal Engine for the 3D visualization of GIS data and the potential application of Unreal Engine in large-scale geo-visualizations. It explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality for working with GIS data, such as the Vitruvio plug-in to implement procedural modeling of buildings. The case study is applied to GIS datasets of the historical Vltava Valley covering an area of 1670 km² to demonstrate the unique challenges of using Unreal Engine to create realistic visualizations of large-scale historical landscapes. The resulting visualizations are presented. The practical application of this research provides insights into the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale historical areas. Full article

Procedural modeling offers significant advantages over traditional methods of geovisualizing 3D building models, particularly in its use of scripts or machine language for model description. This approach is highly suitable for computer processing and allows for the rapid rendering of entire building models and cities, especially when the buildings are not highly diverse, thus fully leveraging the strengths of procedural modeling. The first hypothesis is that buildings in the real world are mostly different and they should still be able to be displayed through procedural modeling procedures, and the second hypothesis is that this can be achieved in several ways. The first hypothesis suggests that real-world buildings, despite their diversity, can still be effectively represented through procedural modeling. The second hypothesis explores various methods to achieve this representation. The first approach involves recognizing the basic characteristics of a building from photographs and creating a model using machine learning. The second approach utilizes artificial intelligence (AI) to generate detailed building models based on comprehensive input data. A script is generated for each building, making reverse procedural modeling in combination with AI an intriguing field of study, which is explored in this research. To validate this method, we compare AI-generated building models with manually derived models created through traditional procedural modeling techniques. The research demonstrates that integrating AI and machine learning techniques with procedural modeling significantly improves the efficiency and accuracy of generating 3D building models. Specifically, the use of convolutional neural networks (CNNs) for image-to-geometry translation, and Generative Adversarial Networks (GANs) for texture generation, showed promising results in creating detailed and realistic 3D structures. This research is significant as it introduces a novel methodology that bridges the gap between traditional procedural modeling and modern AI-driven techniques. It offers a robust solution for automated 3D building modeling, potentially revolutionizing the fields of urban planning and architectural design by enabling more efficient and accurate digital representations of complex building geometries. Full article

This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O₃ and NO₂. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O₃ significantly decreased between September and November 2019 and between March and May 2020, while NO₂ significantly increased. NO₂ levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events. Full article