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High-definition (HD) maps are essential for safe and reliable autonomous driving, but their growing size and the need for real-time updates pose significant challenges for in-vehicle storage and communication efficiency. This study proposes a lightweight and scalable HD map provisioning system based on Geohash spatial indexing and Smart Roadside Units (Smart RSUs). The system divides HD map data into Geohash-based spatial blocks and enables vehicles to request only the map segments corresponding to their current location, reducing storage burden and communication load. To validate the system’s effectiveness, we constructed a simulation environment where multiple vehicle clients simultaneously request map data from a Smart RSU. Experimental results showed that the proposed Geohash-based approach achieved an average response time (RTT) of 1244.82 ms—approximately 296.3% faster than the conventional GPS-based spatial query method—and improved database query performance by 1072.6%. Additionally, we demonstrate the system’s scalability by adjusting Geohash levels according to road density, using finer blocks in urban areas and coarser blocks in rural areas. The hierarchical nature of Geohash also enables consistent integration of blocks with different resolutions. These results confirm that the proposed method provides an efficient and real-time HD map delivery framework suitable for dynamic and dense traffic environments.

Many countries where the industrial development and production rates are high face many side effects of low air quality and air pollution. There is an evident correlation between the topographic and climatic properties of a location and the air pollution and air quality on that location. As the variation of air quality is dependent on location, air quality information should be acquired, utilised, stored and presented in form of Geo-Information. On the other hand, as this information is related with the health concerns of public, the information should be available publicly, and needs to be presented through an easily accessible medium and through a commonly used interface. Efficient storage of time-varying air quality information when combined with an efficient mechanism of 3D web-based visualisation would help very much in dissemination of air quality information to public. This research is focused on web-based 3D visualisation of time-varying air quality data. A web based interactive system is developed to visualise pollutant levels that were acquired as hourly intervals from more than 100 stations in Turkey between years 2008 and 2017. The research also concentrated on visualisation of geospatial high volume data. In the system, visualisation can be achieved on-demand by querying an air pollutant information database of 10.330.629 records and a city object database with more than 700.000 records. The paper elaborates on the details of this research. Following a background on air quality, air quality models, and Geo-Information visualisation, the system architecture and functionality is presented. The paper concludes with results of usability tests of the system.

The main aim of this paper is to introduce a solution for network analysis based on pgRouting to resolve the bicycle accessibility of forested green spaces. The proposed application uses open-source software tools such as PostgreSQL with PostGIS extension. The solution includes a complete description of how to perform network analysis using a spatial database with SQL and pgRouting. The implemented functionalities consist of solutions for finding the equidistance or isochrone area for any selected point location. The method is tested on case study data drawn from a total of 9,500 km of roads suitable for cyclists in the Poznań Metropolitan Area, located in western Poland. The results of the analysis were isochrones determining the bicycle accessibility of forested areas. The accessibility analysis was performed considering an urbanised residential area. As a result of the analysis, locations with the best and limited access to forested green areas were identified. Moreover, the described methodology is ready to be used to solve various accessibility problems.

Existing nationwide spatial datasets for Germany’s traffic infrastructure, particularly parking areas, are fragmented and incomplete, hindering effective traffic management and urban planning amidst growing demands for mobility transition and livable cities. This paper presents a novel approach to create a comprehensive parking area inventory for Berlin using aerial imagery. The methodology integrates AI-based traffic area segmentation and DINO-based vehicle detection with cadastral data. A key innovation is a workflow that classifies parking areas by their orientation and accessibility for refined capacity calculation. The resulting Berlin-wide inventory comprises 1,333,953 parking spots. Our method significantly contributes by mapping private (19 %) and semi-private (21 %) parking areas, which are largely missing from existing inventories, alongside publicly accessible (60 %) spaces. Vehicle detection identified 1,039,155 vehicles (1,019,690 LDV, 19,465 HDV). Initial classification shows 36 % parallel, 27 % diagonal, 20 % vertical, and 17 % unclassified parking spots, with notable variability across districts. This comprehensive inventory addresses a critical data gap, providing a more accurate understanding of urban parking resources. The highly automated and repeatable nature of this aerial imagery-based approach offers significant potential for large-scale applications and temporal change analysis. Future work will focus on developing correction factors for capacities in partially occluded areas and integrating information on underground parking facilities to further enhance completeness.

Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring of the urban environment. This research presents a workflow for mapping urban land use at the street block level, with a focus on residential use, using very-high resolution satellite imagery and derived land-cover maps as input. We develop a processing chain for the automated creation of street block polygons from OpenStreetMap and ancillary data. Spatial metrics and other street block features are computed, followed by feature selection that reduces the initial datasets by more than 80%, providing a parsimonious, discriminative, and redundancy-free set of features. A random forest (RF) classifier is used for the classification of street blocks, which results in accuracies of 84% and 79% for five and six land-use classes, respectively. We exploit the probabilistic output of RF to identify and relabel blocks that have a high degree of uncertainty. Finally, the thematic precision of the residential blocks is refined according to the proportion of the built-up area. The output data and processing chains are made freely available. The proposed framework is able to process large datasets, given that the cities in the case studies, Dakar and Ouagadougou, cover more than 1000 km2 in total, with a spatial resolution of 0.5 m.

Wildfire monitoring in tropical regions requires robust frameworks capable of transforming heterogeneous satellite detections into consistent, event-level information suitable for decision support. This study presents the DescrEVE Fogo (Descrição de Eventos de Fogo) framework, a relational and scalable system that models daily fire events in Brazil by integrating Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections within a unified Structured Query Language (SQL)/PostGIS environment. The framework formalizes a mathematical and computational model that defines and tracks fire fronts and multi-day fire events based on explicit spatio-temporal rules and geometry-based operations. Using database-native functions, DescrEVE Fogo aggregates daily fronts into events and computes intrinsic and environmental descriptors, including duration, incremental area, Fire Radiative Power (FRP), number of fronts, rainless days, and fire risk. Applied to the 2003–2025 archive of the Brazilian National Institute for Space Research (INPE) Queimadas Program, the framework reveals that the integration of VIIRS increases the fraction of multi-front events and enhances detectability of larger and longer-lived events, while the overall regime remains dominated by small, short-lived occurrences. A simple, prototype fire-type rule distinguishes new isolated fire events, possible incipient wildfires, and wildfires, indicating that fewer than 10% of events account for more than 40% of the area proxy and nearly 60% of maximum FRP. For the 2025 operational year, daily ignition counts show strong temporal coherence with the Global Fire Emissions Database version 5 (GFEDv5), albeit with a systematic positive bias reflecting differences in sensors and event definitions. A case study of the 2020 Pantanal wildfire illustrates how front-level metrics and environmental indicators can be combined to characterize persistence, spread, and climatic coupling. Overall, the database-native design provides a transparent and reproducible basis for large-scale, near-real-time wildfire analysis in Brazil, while current limitations in sensor homogeneity, typology, and validation point to clear avenues for future refinement and operational integration.

Effective planning, management, and optimization of electrical distribution networks are essential components of modern smart city development and energy sustainability. This study presents a geospatial decision-support system for the low and medium voltage (LV/MV) network of Elektrokrajina d.o.o., the main electricity distributor in Republika Srpska. Model of the system is built upon the extended INSPIRE Utility Network – Electricity model. It leverages a 2D/3D WebGIS platform (ELMAP) to visualize, analyze, and manage electrical infrastructure using open-source technologies such as PostgreSQL/PostGIS, pgRouting, Geoserver and Mapbox. The integration of real-time and semi-structured data from sensors and enterprise systems (e.g., SAP, MDM) is enabled via service-oriented architecture and parallelized query execution. Particular attention is given to addressing topological inconsistencies in legacy data, reconstructing network topology, and linking meter positions to buildings using GPS and OSM data. Custom algorithms were developed for voltage-level tracking, meter reading route optimization, and network loss analysis, including indicators such as SAIFI, SAIDI, and peak loads. The platform enables spatial identification of critical infrastructure points and supports strategic decisions regarding transformer zoning, network upgrades, and demand management. This research highlights the importance of structured 3D geospatial data and demonstrates how scalable, open-source WebGIS systems can support the energy sector in meeting smart grid and sustainability goals.

This paper investigates the challenges of implementing the CityGML standard within database environments for managing urban digital twins (UDT), with a particular focus on addressing big geodata issues. CityGML stands as a critical standard for representing and exchanging 3D urban models and thematic data, essential for effective urban planning and infrastructure management. However, integrating CityGML into databases poses challenges due to the dual requirements of geospatial and semi-structured data. While the former imposes a certain rigor in its formalism, the second benefits from more flexibility. Through a comprehensive review and benchmarking of existing database implementations, including both new distributions and those often use, 3DCityDB, CJDB, Measur3D, and Cerbere, this paper proposes a novel approach towards a multi-database CityGML environment tailored to the specific needs of UDT. The proposed solution leverages the strengths of both relational and NoSQL databases, offering a flexible and scalable architecture while ensuring data consistency, geospatial capabilities and compliance with the CityGML schema. The research hypothesis suggests integrating Cerbere, a middleware for CityGML schema compliance and transaction validation, with 3DCityDB and Measur3D (MongoDB). This approach aims to demonstrate the feasibility of managing advanced 3D data operations based on the CityGML model (3DCityDB) and scalability for big data like IoT (Measur3D) within a multi-database environment. The paper contributes to the advancement of UDT by providing a comprehensive solution for managing diverse data types, facilitating more effective urban planning, infrastructure management, and sustainable development initiatives in the context of smart cities.

Glacier monitoring is a key component in understanding climate change, especially for small and rapidly retreating glaciers in regions like the Alps. These ice bodies, though limited in size, play a crucial role in local water resources, ecosystem stability, and natural hazard management. However, their fragmented terrain poses significant challenges for data collection and interpretation. This study presents a WebGIS platform developed for the Belvedere Glacier (Italian Alps), designed to enhance data accessibility, visualization, and analysis through a low-cost, open-source solution. The platform integrates heterogeneous datasets — including GNSS measurements, displacement, velocities and acceleration time series — using CesiumJS for 3D geospatial visualization and PostgreSQL/PostGIS for spatial data management. It allows users to explore monitoring points, visualize glacier dynamics, and perform comparative temporal analyses via an intuitive interface. Built entirely with Free and Open Source Software for Geospatial, the system supports both data upload and export, promoting collaborative workflows and reproducibility. Designed with usability in mind, the platform targets a broad audience, from researchers to policymakers, and demonstrates the potential of WebGIS to support long-term glacier monitoring. The proposed architecture is transferable to other environmental applications, contributing to the digital transition in climate impact documentation.

Processing Earth observation data modelled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle the processing, and new approaches are needed to analyse these datasets. The objective of this work is to develop methods to interactively analyse big raster datasets with the goal of most efficiently extracting vector data over specific time periods from any set of raster data. In this paper, we describe RINX (Raster INformation eXtraction) which is an end-to-end solution for automatic extraction of information from large raster datasets. RINX heavily utilises open source geospatial techniques for information extraction. It also complements traditional approaches with state-of-the- art high-performance computing techniques. This paper discusses details of achieving big temporal data extraction with RINX, implemented on the use case of air quality and climate data extraction for long term health studies, which includes methods used, code developed, processing time statistics, project conclusions, and next steps.