ABSTRACT. Digital Twin is a virtual representation of a physical object or system that uses real-time data to simulate its behavior without direct interaction with the real world. In recent years, the digital twin concept has been increasingly applied in geospatial sciences, where it enables advanced modeling, analysis, and visualization of complex spatial phenomena. This paper presents the concept of a spatial digital twin aimed at supporting activities related to crisis management, including fire response, flood risk assessment, and military operations. The main objective of the study is to propose a conceptual framework for the development of a spatial digital twin that accurately reflects a real geographical environment. Particular attention is given to the role of cartographic and GIS data in building a realistic virtual representation of space. The research focuses on spatial datasets available for the territory of Poland, which is treated as a representative example of a country with relatively well-developed spatial data infrastructure. The conducted research includes a classification of available spatial data sources with respect to parameters essential for crisis management applications, such as level of detail, update frequency, spatial coverage, and the availability of descriptive attributes. These factors directly affect the reliability and usability of a spatial digital twin in decision-support processes. The analysis highlights the importance of high-quality cartographic data and consistent GIS layers in creating an integrated virtual environment. The paper also discusses limitations of existing spatial datasets and identifies key challenges related to the development of spatial digital twins in areas with different levels of economic development and data availability. A significant issue addressed is data integration, particularly the problem of redundancy and inconsistency among datasets originating from multiple spatial repositories. The study emphasizes that effective harmonization of heterogeneous spatial data is a critical prerequisite for building operational spatial digital twins. The presented findings contribute to ongoing discussions on the role of cartography and GIS in advanced spatial modeling and crisis management systems.

ABSTRACT. The article presents a methodology for collecting and processing spatial data for the creation of a typology of European regions. Both the methodology and the resulting typology are being developed as part of the international GoDigiBioS project. The main objective is to create a robust, data-driven system for classifying areas based on complex interactions between biodiversity, society, the economy, and technological development. The process begins with the careful identification and selection of data sets that describe static and dynamic biodiversity, socioeconomic prosperity, public administration, and technological progress. The spatial delimitation of the analysis uses the administrative boundaries of NUTS (Nomenclature of Territorial Units for Statistics), with the initial typology working with NUTS 0 level and the final version targeting more detailed NUTS 2 or NUTS 3 levels, depending on data availability. A key phase in the creation of the typology is data harmonization, which includes verification, metadata completion, consolidation, imputation, and normalization of values. For subsequent clustering, the article describes the use of advanced statistical methods and a hybrid approach based on principal component analysis (PCA). The methodology includes a comparison of different methods (Elbow, Silhouette) for identifying the optimal number of clusters and hierarchical clustering (Ward's method) as well as non-hierarchical clustering (K-means) for creating groups of regions. The result is the division of European regions into specific clusters (e.g., innovation leaders, stable developing countries, or tourism-based areas), which enables the effective creation of thematic maps. The entire process is aimed at creating the Data Explorer tool, which visualizes biodiversity data and supports decision-making processes in nature conservation. This modular framework is designed so that it can be gradually optimized and supplemented with new data over time.

ABSTRACT. Transportation is the foundation of sustainable urban development. The sustainability of a city's society and economy largely depends on the smooth and efficient operation of public transportation. Tbilisi, the capital of Georgia, is a city with a complex layout. Its complex topography complicates transportation between districts and functional areas of the city. For socio-economic and infrastructural reasons, traffic jams often occur, and in such conditions, organized public transportation management is of great importance.

In 2007, we proposed to the Tbilisi City Hall to implement a GIS public transportation dispatching system. In the first stage, existing bus routes and their infrastructure (e.g., bus stops) were digitized in GIS format. This data later formed the basis for the Tbilisi Transport Company's interactive web map, which consists of the following sections: trip planning, schedules and routes, real-time buses, stops, and the Tbilisi bus garage.

The aforementioned web map, along with signs installed at bus stops, is a very useful social service that makes it easier for passengers to navigate the city. Furthermore, planning new routes (with stops) or optimizing existing routes is accomplished using the transport network's geographic information system. Isochronous digital maps and digital maps with traffic signals are effectively used for this purpose.

Today, geographic information systems play a vital role in the sustainable development of public transport in Tbilisi, and in the future, they will enable the use of similar systems not only in the capital, but also in other major cities across the country.

ABSTRACT. The rapid expansion of both open and authoritative spatial datasets has increased the demand for objective, repeatable and scalable methods for their quantitative comparison. Building layers are fundamental to urban analysis, spatial planning and digital twin applications, yet they are a key layer in many spatial data sources. Consequently, they frequently differ in geometric accuracy, completeness and semantic consistency. Despite this, dataset comparison is still commonly based on manual inspection, visual assessment or simple positional metrics, which limits the depth and reliability of analyses. This paper introduces an automated framework for the comprehensive comparison of building spatial datasets. The proposed approach integrates complementary metrics that jointly evaluate quantitative, geometric, attribute-based and topological characteristics of building layers. It automatically derives building counts, area and volume statistics, geometric similarity measures, positional deviations, indicators of attribute completeness and consistency, as well as topological relationships between neighboring objects. A key contribution of the framework is the identification of a set of metrics that comprehensively characterize differences between datasets, together with the development of an automated analysis framework. The multi-metric approach enables discrepancies to be interpreted within a broader analytical context, allowing their nature, scale and potential causes to be better understood. The method supports both one-to-one and many-to-many building matching, accounts for partial overlaps, and is robust to differences in data resolution and source specifications. The methodology is tested on multiple real-world building datasets, including the Topographic Objects Database, OpenStreetMap and Vector Map Level 2 data. Results show that the proposed approach enables a more comprehensive understanding of discrepancies than traditional single-metric comparisons. Overall, the framework provides a scalable and reproducible solution for dataset validation, quality assessment and integration in urban analytics, cadastral maintenance and digital twin development.

ABSTRACT. The creation of maps of indoor spaces and environments is becoming increasingly important. In the past, people spent most of their time outdoors, where one of their major challenges was finding their way around and orienting themselves. Maps, both topographic and thematic ones, were therefore an important source of information that helped them make appropriate decisions in space. Today, more and more human activities take place indoors. These are no longer just homes or workplaces, where we are familiar with the environment and can easily orient ourselves, but also large indoor spaces such as shopping centers, parking garages, educational institutions, administrative buildings, and cultural institutions. In addition, outdoor navigation is now largely supported by the use of navigation devices that combine digital maps with GNSS positioning services. Indoors, these systems also mostly fail due to limited access to the GNSS signal. For all these reasons, we still need appropriate maps for successful indoor orientation. In the past, highly schematic representations were mostly used, sometimes simplified architectural plans, which were either too vague or too detailed, but above all, they were not standardized in displaying the characteristic challenges of multi-level environment and were not adapted to users. The presentation will introduce a design concept for indoor maps, taking into account all the specific features of multi-level built environments, primarily intended for orientation in indoor spaces. The proposed solutions, if accepted and implemented, could become a welcome guideline for the design of indoor maps for various purposes.

ABSTRACT. Maps and atlases represent essential tools in geography education. This study examined the content of three school world geographic atlases listed in the Slovak Register of Educational Publications in relation to the official requirements defined by the Ministry of Education, Research, Development and Youth of the Slovak Republic. The main objective of the study was to identify human geography topics included in educational curricula for primary and secondary schools and to compare them with the thematic content of chosen atlases. Within the analyzed sample of three atlases, a total of 41 human geography topics were identified and classified into six thematic groups. Based on the comparison, the study proposes a set of thematically and contextually relevant world maps from the field of human geography that were either missing or underrepresented in the analyzed atlases, with a particular focus on environmental topics, as well as on themes such as standards of living or UNESCO World Heritage sites. The proposed atlas content was created primarily using open-source datasets, with an emphasis on streamlining data extraction, processing, and application within the ArcGIS environment. The paper also documented the entire cartographic workflow and addressed some common challenges, such as the translation of English-language datasets into Slovak map content and the classification of human geographic phenomena. Finally, the study explores the potential of various infographic and cartographic visualization methods to highlight key information for pupils and enhance overall map usability and user experience in school world geographic atlases.

ABSTRACT. Forest inventory and forest silviculture research require detailed information on tree characteristics. Conventional methods for measuring dendrometric parameters are simple and sometimes destructive, but they are limited by accuracy and efficiency in the field. In contrast, the use of Lidar TLS (Terrestrial Laser Scanning) offers increased accuracy and efficiency. In addition, Lidar TLS data can be used to generate detailed 3D models of tree geometry, which can be further analyzed to extract additional information on tree growth and health. These measurements can provide valuable information on the structure of the plant community, forest productivity and biodiversity of the studied ecosystem. This work explored the potential of Lidar TLS to accurately measure dendrometric parameters in forest stands. Knowledge of the dendrometric parameters of a Pinus halepensis is necessary for its sustainable conservation. It allows assessing the available resources and to fight against its destruction. The results demonstrate the effectiveness of this technology to provide accurate and detailed information on tree structure. The values of dbh (Diameter at Breast Height) are estimated in our studies vary from 9.0 cm to 24.7 cm while the heights vary between 4.651 m and 9.989 m, the TLS Lidar technique allowed us, on the one hand, to estimate the volume of the trunk in our study varies between 0.026 m3 and 0.251m3 and, on the other hand, the volume of the branches which vary from 0.001m3 to 0.602m3. The analysis of these data makes it possible to describe the structure of forest plots, to study the interactions between individuals and to establish allometric relationships. Modeling also proves to be an effective and non-destructive means of quantifying the distribution of plant material. The results of this study suggest that TLS Lidar is a valuable tool for forest management and inventory. By providing accurate and detailed information on tree structure, Lidar TLS can help forest managers make informed decisions about forest management practices and sustainable resource use.

ABSTRACT. The Sustainable Development Goals (SDGs), adopted by the United Nations in 2015, have become a central global framework for addressing major social, economic, and environmental challenges by 2030. Over recent years, the SDGs have increasingly influenced public policies, corporate strategies, and investment decisions worldwide, including within the mining sector, where sustainability and transparency are now key priorities. In this context, Environmental, Social, and Governance (ESG) criteria complement the SDGs by providing measurable standards for assessing corporate impact and long-term resilience. This paper explores the role of Geographic Information Systems (GIS) and remote sensing technologies as essential tools for supporting the implementation of SDGs and ESG principles in the mining industry. Through an analysis of international case studies from China, India, Peru, Australia, and Kyrgyzstan, the paper demonstrates how satellite data, unmanned aerial systems, and spatial analysis contribute to effective environmental monitoring, land reclamation, biodiversity protection, and the detection of illegal mining activities. The presented examples illustrate that the integration of vegetation indices, biophysical parameters, and time-series analysis enables objective and transparent assessment of environmental recovery processes. In addition, the paper highlights a Bulgarian case study from Elatsite-Med AD, showcasing how GIS, remote sensing, and innovative technologies such as unmanned aerial vehicles are successfully applied in practice for land reclamation and tailings storage facility monitoring. The analysis emphasizes that modern geospatial technologies are no longer solely research tools, but have become an integral component of sustainable and responsible mining management, supporting informed decision-making, regulatory compliance, and long-term environmental stewardship.

ABSTRACT. Land and building cadastral maps and registers maintained in geoinformatics systems include data on property ownership boundaries. The Land and Building Cadastre (LBC) in Poland is the basis for surveying, as well as legal and administrative processes. Errors in this data can lead to inaccuracies in ownership issues, land division, property boundary determination, and spatial planning. Accurate geodetic data are the basis for developing infrastructure projects such as roads, bridges, and railways, as well as for spatial planning and urban development. Errors in the cadastral records can lead to the need for project corrections, which increases the costs and time of investment implementation. The development of GIS, photogrammetry, laser scanning, and other modern geodetic tools is increasingly linked to improving the quality of land cadastral data. Systems for identifying and eliminating errors can support these technologies, ensure better integration of spatial data and streamline their use in scientific research and practical engineering applications. High-quality data on land and buildings is essential for spatial planning that incorporates the principles of sustainable development. Record errors can lead to inappropriate land use, which has negative consequences for environmental protection, urban development, and natural resource management. This research presents an original identification and classification of errors in the land and building records in the Kraków County area of Southern Poland. The complexity of this issue and its implications for various sectors of the economy are presented.

ABSTRACT. Relief maps play a crucial role in geo-science education by facilitating the understanding of topography, geomorphological processes, and spatial relationships between natural features. This paper presents an overview and educational evaluation of relief maps of Bulgaria, emphasizing their application in teaching geography, geology, and Earth sciences at different educational levels. Bulgaria’s diverse terrain—ranging from extensive plains and river valleys to complex mountain systems—provides an excellent case study for demonstrating fundamental geomorphological concepts. The study analyzes traditional and modern relief map types, including physical, shaded relief, hypsometric, and digital elevation-based models, highlighting their strengths and limitations in an educational context. Special attention is given to the integration of relief maps with Geographic Information Systems (GIS), digital learning platforms, and interactive visualization tools, which enhance students’ spatial thinking and conceptual understanding. The paper also discusses best practices for incorporating relief maps into geo-science curricula and suggests guidelines for selecting appropriate map scales and representations. The findings underline the importance of relief maps as effective educational resources and support their continued development and use in geo-science education in Bulgaria.

ABSTRACT. The rapid advancement of agentic artificial intelligence opens new possibilities for automating complex geospatial workflows that traditionally required specialized technical expertise. This paper presents the design and implementation of an agentic GIS platform that enables users to visualize spatial data and generate interactive maps through natural language interaction. The system integrates a large language model as a central reasoning component responsible for interpreting user intent, selecting appropriate cartographic visualization types, and autonomously coordinating data retrieval and processing pipelines. To demonstrate the platform's capabilities, real earthquake data for Croatia was fetched from the USGS public API and visualized across multiple map types, including bubble maps, heat maps, and hexagonal binning choropleth maps. The results indicate that the agent can successfully interpret diverse natural language prompts and map them to contextually appropriate visualizations without requiring GIS knowledge from the end user. Limitations and directions for future research are also discussed.

ABSTRACT. Maps are an invaluable tool for understanding our environment. A special kind of maps in that regard are old maps which allow for peering into the past. This allows for a better understanding of contemporary processes. Nowadays there are more and more old maps being scanned into digital repositories, which although stands as a big improvement over classical techniques employed in map analysis, still doesn’t utilize the full potential of this source of spatial information. Only vectorization of features on a map is able to unlock the full capabilities of a GIS enabled digital workspace. Unfortunately preparing this data for analysis is a laborious task which often doesn't justify the cost. That is why there exists a natural need to automatize as much of this process as possible. Automatic digitization of maps is not a new idea, with first attempts starting in the late 80s, but what seems to finally allow for the leap in cartography is Artificial Intelligence (AI), especially Deep Learning techniques (DL). This new tool promises to understand maps in a human-like way and as such allow for accuracy unachievable until now. This study proposes a full methodology for automatically vectorizing maps using the UNet model. The chosen research material encompasses various small-scale maps for the territory of Poland. For comparability with future studies the model has been qualitatively and quantitatively evaluated. The following analysis is the first part in a broader series of studies evaluating the efficiency and usability of AI and DL for automatic vectorization.

ABSTRACT. The rapid development of generative artificial intelligence (GenAI) has enabled the automatic creation of thematic maps based on textual descriptions, significantly transforming the traditional cartographic workflow. By transferring key design decisions—such as data representation, aggregation, symbolization, and visual composition—to algorithmic systems, these tools raise important questions regarding the quality, correctness, and credibility of AI-generated cartographic outputs. In particular, GenAI-generated maps may contain information distortions that differ in nature from errors observed in conventionally produced maps.

The aim of this study is to identify, classify, and quantitatively analyze information distortions occurring in thematic maps generated by GenAI tools. The analysis is based on a dataset of 704 thematic maps produced by eight widely used generative models. The maps cover multiple thematic categories, spatial extents, and levels of data aggregation. Each map was evaluated using a set of expert-weighted cartographic quality criteria addressing semantic-informational, structural-spatial, and compositional-descriptive elements.

The analytical framework combines descriptive statistics with advanced quantitative methods, including repeated-measures analysis of variance, Pearson correlation analysis, and hierarchical cluster analysis. This approach enabled the identification of systematic differences between individual cartographic criteria and the detection of stable clusters of quality components.

The results reveal pronounced differences in the quality of AI-generated maps across individual cartographic elements. The lowest scores were observed for thematic data correctness, legends, and geographic naming, while higher evaluations were generally assigned to overall composition and map extent. The type of GenAI tool used proved to be the most influential factor differentiating map quality, whereas topic and color scale played a secondary role. The findings demonstrate that the quality of AI-generated maps cannot be adequately described using a single aggregate index and instead requires a multidimensional evaluation approach. The study provides a quantitative foundation for further research on user trust and the credibility of cartographic representations generated by artificial intelligence.

ABSTRACT. The Federal State Information System "Unified Digital Platform "National Spatial Data System" (NSDS, System) is the first federal geoinformation system in Russia that ensures the collection, processing and analysis of cross-sectoral spatial data to support informed management decisions in projects ranging from urban planning activities to environmental monitoring. The System utilizes diverse sources of spatial data ensuring a comprehensive approach to solving tasks in various fields. The received data is formed into sets of layers for specific tasks – specialized and thematic. NSDS contains the basemaps necessary for the operation of systems and services. The basemaps are publicly accessible and cover 100% of the tasks of a regular user. Limited users have access to a number of basemaps and Earth remote sensing data on a larger scale to solve specific tasks. The main basemaps are the Unified Digital Basemap (systematized spatial data map based on topographic maps and digital orthophotos) and the Digital Object Scheme, which serves as a basis for searching, analyzing, data verification, and working with them within the services. Currently, the System contains 4191 spatial data layers that are filled with information ranging from the composition of title documents to specialized data. 26 client-centric services are in operation aimed at both a wide range of users and professionals in real property, geodesy and cartography. We plan to actively develop new services, providing users with access to modern tools for analyzing and visualizing spatial data. This will increase the efficiency of working with data and improve their application in various fields such as urban planning, ecology and economic activity. NSDS includes AI tools for analyzing data. The Smart Cadastre service uses machine learning capabilities to identify real property units that are not registered in the Unified State Register of Real Property and other violations in land use, determined on the results of neural network analysis of remote sensing data.

ABSTRACT. The cultural-context map represents a conceptual and technological innovation of digital cartography and cultural mapping research in AI era. In this paper, we propose an AI-driven framework coupling a spatio-temporal knowledge graph (SKG), cultural Big Data and an AI chatbot for smart cartography of cultural-context map. From a theoretical perspective, this paper defines the concept and characteristics of the cultural-context map, and puts forward new requirements for digital cartography. In terms of methodological approaches, technical means and empirical studies, taking the Jiangnan region (also Yangtze River Delta region) as the study area, which endowed with profound historical and cultural heritage and distinctive natural and humanistic landscapes, is one of the most brilliant regions in China and enjoys a high reputation internationally. this paper developed an ontology-based construction method in establishing a spatiotemporal knowledge graph of Jiangnan water towns. Secondly, it employs thematic knowledge discovery techniques to facilitate the thematic design and compilation of cultural context maps. Finally, it develops an AI chatbot model to carry out the intelligent mapping practice of the cultural context map of Jiangnan regional culture.

ABSTRACT. In this paper, a rather unknown side of Raisz Erwin’s wide cartographic interest is presented trough a specific example motivated by his close professional relationship with Cuban geography and cartography, which officially began with the making of the Physiographic Diagram of Cuba in collaboration with Cuban professor Salvador Massip in 1929 and lasted for more than 30 years. The data at our disposal allows the author to reconstruct the initial stage of this collaboration, because the work related to the Physiographic Diagram was part of a larger and more ambitious project: the publication of the first textbook on Geography of Cuba for secondary and higher education. Massip, who studied with Raisz at Columbia University in New York and was President of the Cuban Geographical Society at the time, invited Raisz to collaborate on the making of this textbook, offering him a very meaningful task that would undoubtedly spark Raisz's interest: his participation as illustrator, drawing not only maps for the book, but also physiographic block profiles, side and bird's-eye representations, as well as statistical diagrams. The book was finished in a relatively short period between 1928 and 1930, but it could only be published 12 years later, in 1942. Raisz developed an educational material that, as is usual in his work, is mostly characterized by his personalized (carto)graphic versatility and quasi-artistic realization. The high quality of his work –which completed the text written by Massip and his wife, Dr. Sarah E. Ysalgué in an excellent and illustrative way– was confirmed not only by the opinions given by Cuban specialists, but also book reviewers of contemporary foreign scientific journals.

ABSTRACT. Digital cartographic education is rapidly incorporating generative AI, yet educators face epistemic risks from Large Language Model (LLM) outputs—especially "hallucinations,”plausible but inaccurate or unfounded claims—that remain under-theorised in map-based inquiry. This study proposes a cartography-focused typology of geographic hallucinations observed in a project-based high-school lesson where digital maps served as the primary representational and analytical medium. The lesson used a Korean geography textbook unit on "geographic conflict" to explore the disputed toponym for the sea between Korea and Japan (East Sea/Sea of Japan). It guided learners to compare claims, interpret map evidence, and articulate supported positions, emphasising multiple perspectives over a single answer. A six-step scaffold—guidelines, iteration, summarisation, inference, transformation, and expansion—helped develop cartographic literacy by enabling systematic question formulation, response evaluation, and perspective exploration. Students entered equivalent prompts across multiple generative-AI platforms, then compared AI-generated narratives to digital basemaps, historical maps, and teacher-curated sources, modelling geographic inquiry. Learners acted as critical map readers and evaluators, playing a central role in geography education. They collected erroneous or misleading information and applied a concise verification protocol to show why claims diverged from cartographic or contextual evidence. Analysis of these errors revealed six common hallucination types with pedagogical relevance: factual (incorrect dates, actors, or attributions), conceptual (misframing naming principles or mapping conventions), creative (invented but plausible-sounding “evidence”), contradictory (internally inconsistent statements), selective (omitting salient counter-claims or map-based context), and irrelevant (off-topic content that disrupts inquiry). Hallucinations mostly occurred when tasks required students to synthesise sources, make inferences, or evaluate complex issues on sovereignty and national interests. This indicates that inference and evaluation tasks are best guided by educators, even in cartographic learning with AI. Including verification as part of digital citizenship emphasises fact-checking as essential in cartographic inquiry, not just remedial. The typology derived from these observations helps educators design meaningful AI-assisted lessons on geopolitical mapping, border disputes, and environmental negotiations, where interpretation relies on historical maps, cultural perspectives, and map literacy.

ABSTRACT. Based on our experience of development and teaching Machine Learning in Geospatial Studies course in 2024-2025 (https://www.hse.ru/en/edu/courses/919043621), Master’s program in spatial data and applied geoanalytics at the Geography and Geoinformation Technologies Faculty, HSE University, we propose its redesign by adding Sections on the LLM and GPT, expert and fuzzy logic systems. Updated course will be consisted of 3 parts: in the first one we present the ML basics by keeping the present experience, then part 2 and part 3 include GeoLLM and GeoGPT, as well as expert systems and fuzzy logic to solve spatial tasks. GeoLLM and GeoGPT’ part includes architectures, training, output optimization and infrastructure, and security. Expert systems and fuzzy logics cover classification, structure and operating principle of expert systems, peculiarities of expert systems, basics of expert systems design, development stages, and approaches to create geospatial expert model. For the fuzzy logic systems we consider fuzzy sets and fuzzy operations, as well as application in geospatial domain. As was done before for the actual version which is being delivered to the student in the beginning of 2026, the course materials are of constant updates with various news, papers, textbooks and specialized conferences on geo-artificial intelligence. The course consists of lectures and practical seminar assignments, will be ready to be taught to the students of HSE-campuses remotely.

ABSTRACT. The rapid evolution of the digital world has significantly transformed the methodology of teaching geography, presenting new challenges to the instructional process. This paper explores the impact of digital platforms on the educational environment, where the uncontrolled dissemination of information makes it increasingly difficult to distinguish between academically reliable resources and inaccurate or misleading content. This ambiguity often leads educators to entirely bypass digital tools in the teaching-learning process. The primary objective of this research is to identify and qualitatively evaluate geography-related video content available on Georgian digital platforms. The study aims to formulate practical recommendations for educators by highlighting reliable, high-quality resources in the Georgian language. Furthermore, the paper emphasizes the importance of accessible digital content in promoting geography as a field of study and a professional career path for the younger generation. The findings suggest that fostering the creation of quality digital content by students, scholars, and educators is crucial for addressing the current shortage of young researchers in the national scientific space. While focused on the Georgian context, the results and methodology of this study offer valuable insights for the global geographical community in addressing similar educational challenges.

ABSTRACT. The inaugural cohort of learners under Kenya's Competency-Based Education (CBE) System has now entered its tenth year at senior secondary education. While subject matter has evolved to include cartography within school career pathways and atlases, map-related instruction in social studies remains insufficient to foster deep appreciation and practical competency in mapping. This study identifies a persistent gap in map-reading proficiency, limiting the application of cartographic products across disciplines despite their critical role in geographical analysis. Data from ongoing teacher surveys and classroom observations reveal inadequate integration of hands-on activities, underscoring the need for targeted cartographic sensitization programs. Recommendations include sustained professional development workshops for geography educators to embed interactive mapping exercises aligned with the competency based curriculum (CBC) competencies. Initiatives like the Cartographer’s Cause, led by the GeoEducation & Outreach Working Group, demonstrate preliminary success in bridging this gap through learner outreach. Enhanced teacher training is essential to leverage cartography's potential in fostering spatial literacy and interdisciplinary connections.

ABSTRACT. In recent years, there has been a growing demand for higher education in a distance learning mode. Mostly due to its location- and time-independent characteristic, it presents a great study opportunity for potential students living in rural areas, working full-time jobs, or those on parental leave. Our study aims to research the current state of distance learning in higher education in Europe and publish an online interactive map visualising the study programmes. By analysing official national sources, we have retrieved data about the availability of such study programmes primarily in Central Europe. Using the collected data, we aim to design and publish a platform that would visualise the spatial distribution of distance learning programmes and would serve as an effective tool for all interested parties (potential students, tutors, university executives, etc.). We see great potential not only in providing detailed information about study programmes but also in opening up new possibilities for stakeholders interested in starting such programmes at their educational institutions.

As the study is still in progress, we are currently designing an interface for the platform using the ArcGIS Experience Builder software. We implement a human-centred approach and data humanism principles for a better user experience. We have conducted two iterations of user testing. Initially, we tested a low-fidelity prototype in Figma, and subsequently, we tested a first version of the interface, which included a landing page and a map of distance learning as its central element. Applying user feedback, we are redesigning the interface by reducing unnecessary subpages, adjusting the size of the widgets accompanying the map, and optimising the function for filtering study programmes. Our future study plans involve another iteration of user testing, publishing the map online and enriching the geo-database with data from other countries.