Search Results (130)

Hyperspectral data from CRISM have proven invaluable for analyzing the mineralogical composition of the Martian surface. However, processing such datasets remains challenging due to their high dimensionality and systematic noise, such as striping artifacts caused by the pushbroom imaging technique. Building on previous research, this study introduces a framework that forms the basis for an automated pipeline that combines preprocessing, dimensionality reduction using UMAP, k-means clustering, and an adaptive stripe correction filter to generate mineral maps of the Martian surface. Additionally, the pipeline integrates a noise variance estimation step based on PCA to assess the feasibility and expected efficacy of stripe removal before applying the filter. We validate the methodology across multiple CRISM datasets, including regions such as Jezero Crater, Nili Fossae, and Mawrth Vallis. Comparative analyses using metrics such as the CH index, DB index, and SC demonstrate improved clustering performance and robust mineralogical mapping, which indicates a step toward more reliable and automated clustering of CRISM data. Furthermore, the pipeline leverages spectral libraries for automated mineral classification, yielding results comparable to expert-defined maps while addressing discrepancies caused by residual noise or clustering limitations. This study represents a step toward fully automated, scalable geospatial analysis of CRISM Martian surface data, offering a robust framework for processing large hyperspectral datasets and supporting future planetary exploration missions. In the future, we intend to deploy an automated analysis pipeline as a freely accessible web service. Full article

This paper presents the concept, implementation, and evaluation of FastFoam—a web-based inspection system designed for the technical assessment of buildings. Developed through international collaboration, FastFoam supports flexible inspection workflows, structured data collection, and integration with classification systems and geospatial data. The system enables civil engineers to create, customize, and manage inspection templates, store inspection results in a centralized database, and analyze inspection data using both descriptive and extensible analytical tools.To assess user needs and guide system development, a nationwide survey was conducted among Polish civil engineering professionals. The results confirmed strong interest in mobile and web-based inspection tools, as well as specific functional expectations regarding template customization, defect documentation, and automated reporting. The system architecture follows a multi-layered design with separate user, server, and external service layers. It supports modular data structures, role-based access, and integration with external platforms such as OpenStreetMap and BIM systems. A key innovation of FastFoam is its implementation of the FOAM (Function-Oriented Assessment Methodology), which enables temporal analysis and prediction of building condition over various timeframes. A case study demonstrates the application of FastFoam in a real-world building inspection in Poland. The evaluation confirmed the system’s practical usability while also revealing opportunities for future enhancements including AI-based defect detection, IoT integration, offline mobile functionality, and open data export. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

Rural tourism influences rural communities, yet its growth often leads to substantial land use changes, creating both opportunities and tensions. Despite this, a comprehensive overview of the literature examining their intersection is absent. To address this gap, this study employed a bibliometric analysis of 497 documents from the Web of Science database spanning 1994 to 2025. Methods included major publication trend analysis, keyword co-occurrence analysis, and co-citation analysis to uncover publication trends, dominant themes, and intellectual structure. Results indicate a rapidly expanding, interdisciplinary field characterized by strong international collaboration and a focus on sustainability, environmental planning, and integrated land management. Key thematic clusters include geospatial tools, environmental stewardship, urbanization impacts, social dimensions, and economic assessment of rural landscapes. The intellectual foundations are rooted in spatial planning, ecosystem services, socio-economic impacts, and ecotourism’s conservation goals. Gaps identified include lack of synthesis studies, underrepresentation of qualitative methods, insufficient policy-implementation research, and underrepresentation of European and intra-Global South collaborations. The study calls for future works to address these gaps through interdisciplinary approaches, longitudinal monitoring, and expanded regional collaborations. By mapping the field’s evolution, this study provides a foundational reference for researchers, policymakers, and practitioners seeking to balance tourism development with sustainable land use in rural areas. Full article

When there is an absence of disaster prevention measures, natural hazards can lead to disasters. An essential part of disaster risk management is the geospatial modeling of devastating hazards, where data sharing is of paramount importance in the context of early-warning systems. This research points out the usefulness of Spatial Data Infrastructures (SDIs) for disaster risk reduction through a literature review, focusing on the necessity of data unification and disposal. Initially, the principles of SDIs are presented, given the fact that this framework contributes significantly to the fulfilment of specific targets and priorities of the Sendai Framework for Disaster Risk Reduction 2015–2030. Thereafter, the challenges of SDIs are investigated in order to underline the main drawbacks stakeholders in emergency management have to come up against, namely the semantic misalignment that impedes efficient data retrieval, malfunctions in the interoperability of datasets and web services, the non-availability of the data in spite of their existence, and a lack of quality data, while also highlighting the obstacles of real case studies on national NSDIs. Thus, diachronic observations on disasters will not be made, despite these comprising a meaningful dataset in disaster mitigation. Consequently, the harmonization of national SDIs with international schemes, such as the Group on Earth Observations (GEO) and European Union’s space program Copernicus, and the usefulness of Artificial Intelligence (AI) and Machine Learning (ML) for disaster mitigation through the prediction of natural hazards are demonstrated. In this paper, for the purpose of disaster preparedness, real-world implementation barriers that preclude SDIs to be completed or deter their functionality are presented, culminating in the proposed future research directions and topics for the SDIs that need further investigation. SDIs constitute an ongoing collaborative effort intending to offer valuable operational tools for decision-making under the threat of a devastating event. Despite the operational potential of SDIs, the complexity of data standardization and coordination remains a core challenge. Full article

Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article

Various platforms, such as Google Maps, provide information about the services of public transport companies worldwide. Operators publish the planned (static) timetable using the General Transit Feed Specification (GTFS) format, while the GTFS Realtime (GTFS-RT) specification provides live (dynamic) information about the services. In this paper, we present our dataset that was built by retrieving and pre-processing the data sources of the open data platform of BKK Futár, hosted by the Centre for Budapest Transport Company (BKK). The paper contains a well-detailed description of our methods for retrieving and pre-processing the data among statistical features. The dataset covers a one-year period in which the data collection mechanism used for realtime data was continuously improved from collecting only live vehicle positions to covering all the available feeds and increasing the query frequency. We merged the static data with the vehicle positions to filter them, yielding a clean set of tracked trips. As a result, more than 90% of the daily planned trips could be reconstructed from the responses. We provide an interactive web-based visualization for the analysis of the GTFS schedule’s, and the GTFS-RT Vehicle Positions feed’s, geospatial features. The dataset and also our methodology can serve as input for various research studies to investigate the common characteristics of delays and disruptions or predict real departure times based on the current vehicle positions and historical data. Full article

This paper aims to analyze and synthesize research on sustainable urban land management (SULM) based on earth observation (EO) data. Particular attention is given to the intellectual foundations and emerging trends in the field. We conducted a search in the Web of Science database, identifying over 1600 research papers, primarily journal articles and conference proceedings. A systematic review methodology was employed for both quantitative analysis (e.g., trends in SULM research over time, distribution by country, journal impact, etc.) and qualitative analysis (e.g., intellectual foundations, emerging trends, and research limitations). An analysis of the 50 most cited publications revealed two main research streams, environmental and technological. The environmental one focuses on the assessment and monitoring of ecosystem services and land use change as a key driver of climate change and its environmental impacts, while the technological stream highlights the role of remote sensing and geospatial technologies and their fusion to develop better, more tailored models and indicators. The researchers also highlight the differences in analytical methodology, depending on the scale of the study. Based on a thorough analysis of the scientific literature, we concluded that sustainable land management, especially in urban areas, is currently the only concept that provides the basis for human survival on earth. Furthermore, monitoring SULM and assessing its changes are immensely difficult without earth observation data. Full article

The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been developed using Python and JavaScript for downloading and converting EGMS data. This revised and updated version improves the functionality and performance of the original R-based desktop tool, avoiding the need for a standalone software installation. Users can now simply access the webapp with an internet connection. In addition, the web version enhances data processing by leveraging high-performance server-side computing without relying on personal computer resources. The EGMStream webapp offers advanced features, including the parallel processing of large datasets and extraction of converted EGMS data for areas of interest (AoI) in various GIS-compatible formats. The transition from standalone software to a cloud-based system streamlines the integration of EGMS data into existing workflows, broadens user accessibility, and supports large-scale geospatial analysis. Consequently, this shift promotes the dissemination of these relevant and free available measurement data to a wider audience, including non-expert users. Full article

Virtual geographic environments long simulated real-world scenarios in urban planning, monument preservation, city infrastructure management, education, and entertainment. Their web-based visualisation and distribution made these environments widely accessible. However, many systems remain static, lacking real-time data integration and multi-user collaboration, while virtual worlds designed for the Metaverse emphasise dynamic interaction yet often omit essential geospatial context. Bridging this gap is critical for advancing virtual geographic environments into the next generation. In this paper, we present a modular system architecture for applications demonstrating geospatial virtual worlds over the web. Our goal is to provide a generic, well-structured framework that exposes the essential classes and interfaces needed for building 3D virtual worlds with geospatial data at their core. Our work focuses on defining specific geospatial components, methods, classes, and interfaces that form the foundation of a modern geospatial virtual environment in the Metaverse era. The proposed architecture is organised into three layers: access, world, and integration, which together enable accurate mapping and integration of real-time sensor data, digital twin synchronisation, and support for location-based services. Our analysis reveals that while most current solutions excel in either multi-user interaction or geospatial data management, they rarely combine both. In contrast, our model delivers enhanced geospatial focus, real-time collaboration, and interoperability between physical and digital realms. Overall, this work lays a solid foundation for future innovations in creating immersive, interactive, and geospatially grounded virtual experiences over the web, marking an important step in the evolution of virtual geographic environments for the Metaverse era. Full article

Massive image datasets are often required for the proper functioning of Machine Learning (ML) and Computer Vision (CV) applications. This paper offers a solution to computational challenges in the Image Processing of satellite imagery, by proposing an optimization procedure. The presented approach is verified by an exemplary Python implementation, constituting a standalone tool for automating the dataset creation and labeling, including the extraction of road network data from the national satellite cartography provider. The collected data include detailed road maps along with the parcel information obtained via WebMapService endpoints. The method presented in this paper involves three basic steps: road segmentation (using the Shapely module) to facilitate handling high-resolution orthoimagery, and then a modified Region-of-Interest approach, i.e., removing irrelevant areas, with only roads remaining. This results in obtaining file sizes that are significantly smaller. The presented algorithm also involves asynchronous tile downloading, which, combined with the masking of irrelevant areas, improves not only the efficiency but surprisingly also the accuracy of subsequent ML/CV procedures. The research results of the paper reveal substantial file size reduction, and improved processing efficiency, thus making the optimized geospatial graphical data more practical for ML/CV applications, while still maintaining the original data quality and relevance of the analyzed parcels or infrastructure. Full article

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

Geographic information systems (GISs) based on WebGIS architectures have transformed geospatial data visualization and analysis, offering rapid access to critical information and enhancing decision making across sectors. This study conducted a bibliometric review of 358 publications using the Web of Science database. The analysis utilized tools, such as Bibliometrix (version R 4.3.0) and Biblioshiny (version 1.7.5), to study authors, journals, keywords, and collaborative networks in the field of information systems. This study identified two relevant clusters in the literature: (1) voluntary geographic information (VGI) and crowdsourcing, focusing on web integration for collaborative mapping through contributions from non-professionals and (2) GIS management for decision making, highlighting web-based architectures, open sources, and service-based approaches for storing, processing, monitoring, and sharing geo-referenced information. The journals, authors, and geographical distribution of the most important publications were identified. China, Italy, the United States, Germany, and India have excelled in the application of geospatial technologies in areas such as the environment, risk, sustainable development, and renewable energy. These results demonstrate the impact of web-based GISs on forest conservation, climate change, risk management, urban planning, education, public health, and disaster management. Future research should integrate AI, mobile applications, and geospatial data security in areas aligned with sustainable development goals (SDGs) and other global agendas. Full article

Urban disparity has been extensively studied using geospatial technology, yet a comprehensive review of GIS applications in this field is essential to address the current research status, potential challenges, and future trends. This review combines bibliometric analysis from two databases, Web of Science (WOS) and Scopus, encompassing 145 articles from WOS and 80 from Scopus, resulting in a final list of 201 articles after excluding 24 duplicates. This approach ensures a comprehensive understanding of urban disparities and the extensive applications of GIS technology. The review highlights and characterizes research status and frontiers into research clusters, future scopes, and gaps in urban disparity analysis. The use of both WOS and Scopus ensures the review’s credibility and comprehensiveness. Findings indicate that most research has focused on accessibility analysis of urban services and facilities. However, there is a recent paradigm shift toward environmental justice, demonstrated by increasing GIS applications in analyzing pollution exposure, urban heat islands, vegetation distribution, disaster vulnerability, and health vulnerability. Full article

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. Full article