Search Results (259)

Interest in Natural Water Retention Measures (NWRMs) for large river basins is growing rapidly as a result of a wide range of benefits, including improved water retention capacity and regulation of ecosystem services. However, suitable site-specific NWRMs in small ungauged basins prone to flash floods and erosion, such as the Vrutci Reservoir Basin in Serbia, have yet to be evaluated and applied, primarily because of a lack of necessary data. The aim of this study was to design an easy-to-implement approach to evaluating the effects of NWRMs on peak discharge, tailored specifically to small basins with significant data gaps. The approach involves developing and analyzing a synthetic unit hydrograph (SUH) based on the available landscape geospatial data and evaluating the effects of NWRMs on the SUH before and after implementation of site-specific NWRMs. This methodological framework allows for quantification of the NWRMs’ effects on the basin and evaluates the proposed measures’ impact to secure better acceptance among stakeholders and informed decision-makers regarding their location in the basin. The results underscore a peak discharge rate reduction from 5% to 33% and hence indicate a positive impact on basin water retention potential. These results highlight the need for support for improved regulating ecosystem services in integrated water management. Full article

In the context of dynamic environmental changes, accurate geospatial information is fundamental for evidence-based decision-making in land-use planning. As urban areas undergo rapid structural transformations, characterizing their spatial morphology becomes essential for assessing ecosystem conditions and identifying pressure points within the urban–rural gradient. Drawing on the indicators for ecosystem condition and pressure recommended by the Mapping and Assessment of Ecosystem Services (MAES) framework, reflecting their trends, this study presents a methodology for comprehensive geospatial mapping of urban and peri-urban morphology, using the Functional Urban Area (FUA) of Burgas, Bulgaria, as a case study. The approach enables multi-scale spatial analysis (regional and local), integrates the structure and functions of urban ecosystems, and reveals the spatial heterogeneity of complex socio-economic systems. At the regional level, ecosystems within the FUA were identified using the national land-use/land-cover database. At the local level, within the city of Burgas, urban morphology was classified by combining building and land-cover types into 14 distinct urban morphological zones (local climate zones—LCZs) using high-resolution unmanned aerial vehicle (UAV)-based orthophotos. This precise spatial data allowed for a detailed assessment of the balance between pervious and impervious surfaces within each LCZ. By integrating Google Earth Engine (GEE) data, the appropriate conditions and pressure indicators in the case study are assessed. Regional ecosystem pressure is effectively captured through the spatial distribution of the Final Pressure Index (IPr). Concurrently, the Urban Ecosystem Performance Index (UEPI) highlights sharp spatial polarization, with critical stress concentrated in the industrial and port zones of the urban core. The results provide policy-makers and stakeholders with critical insights into current pressures and environmental changes in urban and peri-urban ecosystems, offering a robust foundation for evidence-based management and climate change adaptation strategies. Full article

Identifying village functional differentiation within rural territorial systems is essential for differentiated rural revitalization and place-based governance. However, existing approaches still lack effective analytical pathways for translating complex rural territorial relations and sparse planning labels into fine-grained measures of rural functional intensity. To address these gaps, this study develops a Few-Shot Hierarchical Graph Representation Learning (FH-GRL) framework. By integrating a Hierarchical Graph Infomax (HGI) model to capture cross-scale village–township–city relational dependencies and an Evidential Deep Learning (EDL) mechanism to map high-dimensional representations into class-specific evidence and Global Percentile Ranks (GPR), the framework supports fine-grained classification and continuous grading of rural functions. Empirical analysis in Pingdingshan City yields three main findings. First, within the present case study, FH-GRL shows more stable performance than traditional flat clustering and local graph models in identifying complex rural functions under limited labeled samples. Second, hierarchical context serves as a spatial calibration mechanism, reducing locally generated noise and improving the identification of village functional differentiation under spatial heterogeneity. Third, rural functional differentiation reflects the combined effects of place-based conditions and potential flow-related interaction conditions. In particular, Center villages show differentiated trajectories between endogenous production or service centers in agricultural plains and exogenous service centers along urban development axes. Overall, this study provides a planning-oriented quantitative framework for diagnosing rural functional differentiation under label scarcity and spatial heterogeneity. The GPR-based outputs can support the identification of high-intensity functional carriers, transitional villages, and general reserve areas, thereby providing diagnostic evidence for differentiated governance and tiered resource allocation. Rather than replacing formal planning judgment, the framework offers geospatially informed support for classified rural governance and more evidence-informed territorial planning. Full article

Cracks are among the most common pavement distresses, and their timely detection is crucial for road maintenance. Existing methods struggle to completely capture elongated and irregular cracks, often resulting in fragmented detection outputs, which leads to the inaccurate assessment of crack length and affects the reliability of pavement condition evaluation. To address this issue, this paper proposes a dual-path crack segmentation network that integrates CNN and Transformers. The CNN branch incorporates a dynamic multi-branch convolution module to enhance the directional perception and structural modeling of elongated cracks. The Transformer branch employs a lightweight DCNv4 module to replace traditional self-attention mechanisms, effectively capturing long-range dependencies while reducing computational complexity. A multi-path fusion module is designed to achieve the collaborative enhancement of dual-path features, improving the semantic representation of continuous crack regions. Additionally, a combined loss function of BCE and Dice is adopted to alleviate the severe class imbalance between crack and background pixels, further improving the completeness of crack segmentation. Experiments on four datasets, including CFD, DeepCrack537, Gaps384, and Crack500, demonstrate that the proposed model outperforms all compared methods in terms of F-score and mIoU. Ablation studies further validate the effectiveness of the dual-path architecture and its key modules in improving performance. Furthermore, in field validation on real road scenarios, the pavement condition index (PCI) calculated based on the proposed method shows an average deviation of only 0.81 compared to manually interpreted ground truth, demonstrating the practical value of continuous crack detection for pavement maintenance assessment. Full article

Background: Out-of-hospital cardiac arrest is a major cause of mortality, and survival depends heavily on rapid defibrillation. Automated external defibrillators (AEDs) can significantly improve outcomes when used before emergency medical services (EMS) arrive, yet access to and use of these devices remain uneven across communities. This study investigates racial and geographic disparities in AED use during EMS encounters in the United States, evaluating differences across racial groups, geographic settings, cardiac arrest status, and patient acuity, irrespective of whether a bystander or EMS personnel applied the device. Methods: This descriptive study used aggregated data from the National Emergency Medical Services Information System (NEMSIS) Public Release Data Cube to compare AED use across racial, geographic, cardiac arrest, and acuity categories. AED use was defined as any documented application during the EMS encounter. Results: The dataset included 106,246 EMS encounters across six racial and ethnic groups. AEDs were applied in 16,688 encounters (15.7%), with substantial variation across demographic and geographic categories. Asian, American Indian or Alaska Native, and Black or African American patients had the highest rates of AED use, while White patients had the lowest rate despite representing the largest share of encounters. Urban areas accounted for most AED deployments, whereas suburban and frontier regions showed markedly lower use, while rural AED use was similar to urban rates. AED application was strongly associated with cardiac arrest and high patient acuity, yet racial differences persisted even within these clinically severe categories. Conclusions: AED use generally aligns with clinical indicators such as cardiac arrest and critical acuity, but meaningful racial and geographic differences were observed, reflecting descriptive patterns rather than confirmed disparities. These patterns should be interpreted cautiously, as the aggregated nature of the dataset limits the ability to determine whether differences reflect inequities, incident characteristics, or EMS system factors. These findings highlight the need for targeted strategies to expand AED access, improve device placement, and strengthen community readiness in underserved areas. Integrating AED availability into broader EMS planning and community outreach may help reduce inequities and create conditions that support improved survival outcomes. Further research using individual-level data and geospatial methods is needed to clarify the drivers of these observed differences and inform equitable prehospital care policies. Full article

Equitable access to rehabilitation services is essential for individuals living with a disability, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate the travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2255 outpatient rehabilitation clinics across 6896 census tracts. Travel times varied substantially, with rural areas experiencing the longest travel burdens. The 2SFCA analysis revealed pronounced disparities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, suggesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

Amid rapid urbanization, the rapid increase in urban vehicles has exacerbated parking scarcity, particularly in areas surrounding hospitals. As the core city of the Huaihai Economic Zone, Xuzhou’s medical institutions serve a broad region spanning 178,000 square kilometers. The pronounced mismatch between parking supply and demand in these areas severely impacts traffic efficiency and public service quality. To address this challenge, this study proposes a data-driven parking resource planning methodology for the identification and planning of informal/shared parking spaces (utilizing underutilized idle spaces) in hospital vicinities, integrating multi-source geospatial data from OpenStreetMap, remote sensing imagery, and field surveys. The methodology involves data preprocessing (e.g., format conversion, building boundary calibration), parking space identification and classification (e.g., buffer zone delineation, vacant land categorization, shape-based division), and layout optimization using a genetic algorithm combined with manual refinement. Applied within a 1 km radius around two hospitals in Xuzhou, the results demonstrate significant improvements in space utilization and provide a scientific basis for temporary parking facility planning. The results provide practical decision support for urban spatial management and temporary parking governance in high-demand public service areas. Full article

The differential distribution of cellular towers of Telkomsel, Indonesia’s largest mobile network operator, in Banten Province, Indonesia, poses challenges to network performance and service reliability. Therefore, we developed a novel hybrid framework that integrates geographic information systems, minimum spanning tree modeling, and signal-weighted k-nearest neighbor classification to assess tower utilization and signal coverage. Leveraging geospatial data from 110 Telkomsel cellular towers and 1000 simulated user nodes, it was found that 2.73% of towers were overloaded and 189 signal blank spots were identified in rural and topographically complex areas. By incorporating both spatial topology and signal strength sensitivity, the developed method outperforms conventional spatial or machine learning approaches in preserving spatial fidelity and supporting infrastructure planning. Despite the use of simulated user data, the framework demonstrates high scalability and adaptability for integration with real-time network performance metrics, enabling dynamic and location-specific telecommunication optimization. Full article

Contemporary urbanisation in hot-arid cities presents coupled challenges related to sustainability, spatial efficiency, and climate resilience. This study applies Research by Design as a preliminary methodological approach to adapt the superblock typology for Riyadh’s Al-Raed neighbourhood, integrating GIS-based territorial diagnosis with Grasshopper parametric iterations. Sixteen geospatial layers, including land use, density, road hierarchy, transit access, service distribution, green cover, and climatic exposure, inform attractor-based scenario generation and a structured comparative evaluation framework assessing regulatory compliance, human scale, connectivity, and environmental and economic feasibility. The resulting loop-and-courtyard configuration reorganises local streets to strengthen first- and last-mile access, shaded pedestrian continuity, and microclimatic comfort, while supporting Saudi Vision 2030 programs, such as the Quality of Life Program, National Transport and Logistics Strategy, Riyadh Public Transport Program, and Saudi Green Initiative. Quantitative spatial indicators are interpreted alongside design-based morphological reasoning to inform spatial decisions, acknowledging climatic and cultural constraints. This study contributes a reproducible, policy-relevant digital workflow for neighborhood-scale urban transformation in Riyadh and comparable hot-arid contexts. As a preliminary Research by Design phase, it structures iterative scenarios and a structured comparative evaluation framework, providing a foundation for subsequent quantitative and empirical validation. Full article

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

Background/Objectives: The Democratic Republic of the Congo (DRC) has a history of low coverage (<50%) with all first-year-of-life vaccines for children aged 12–23 months, resulting in frequent outbreaks of vaccine-preventable diseases. In response, the DRC’s Expanded Program on Immunization (EPI) is applying innovations to improve vaccination coverage, including using geospatial data to inform vaccination planning (geo-referenced microplans). This report describes a proof of concept to geo-locate, by locality of residence, never-vaccinated children (NVC) or incompletely vaccinated children (IVC); use those data to prepare geo-referenced microplans for rounds of Periodic Intensification of Routine Immunization (PIRIs); and implement the PIRIs. Methods: In 2022, in Kindu and Kibombo Health Zones (HZs), Maniema Province, DRC, children aged 0–23 months were enumerated with inquiries about their vaccination status and reasons for non-vaccination by locality of residence. The enumeration was coupled with the collection of the localities’ geographic coordinates, facilitating the spatial illustration of estimated proportions of NVC by locality. Coordinates for HZ and health area (HA) landmarks and borders were also collected. We created maps that informed geo-referenced PIRI microplans, placing an emphasis on deploying vaccination teams to localities with high proportions of NVC, especially those in remote and riverine locations. To account for inclusion of children aged up to 59 months in the PIRIs, enumeration data were extrapolated to estimate the numbers of NVC and IVC in this wider age range. Volunteers mobilized communities for the PIRIs, HA staff vaccinated age-eligible children, and vaccination teams were geographically tracked. Results: In Kindu, 29,837 children aged 0–23 months were enumerated in 430 localities; among them, 38% were NVC and 6% IVC. In Kibombo, 9582 children aged 0–23 months were enumerated in 168 localities; among them, 50% were NVC and 16% IVC. In both HZs, reasons for never vaccination were primarily associated with knowledge- or belief-related factors, while reasons for incomplete vaccination were associated with access-related factors. Between HAs and localities, there was heterogeneity in the proportions of NVC and IVC and in the reasons for non-vaccination. The numbers of NVC and IVC aged 0–59 months were estimated at 28,220 and 4613 in Kindu and 12,038 and 3785 in Kibombo. Approximately 2000 health staff and community volunteers were engaged for implementation of each of the three PIRIs. The number of children vaccinated during the three PIRIs ranged from 15,500 to 26,500 and from 10,500 to 15,500 in Kindu and Kibombo, respectively. Data suggest that vaccinated children originated from >90% of localities identified during the cartography. Tracking data showed that vaccination teams visited localities with high proportions of NVC, including those that were remote and riverine. Conclusions: Geo-referenced microplanning with engagement of health staff and communities succeeded in vaccinating at least 40,000 children who were not routinely benefiting from health services in two HZs in the DRC; similar innovative strategies could be considered elsewhere. Applying new technologies to existing microplanning strategies can enhance their success. Full article

The automation of public services represents a key area of development at the national level, with the main goal of facilitating citizens’ access to comprehensive, integrated and high-quality services in the shortest possible time. National strategies emphasize the need to integrate open geospatial data and artificial intelligence into information, transparency and decision-making processes. The evolution of artificial intelligence, particularly large language models (LLMs), has led to the development of virtual assistants capable of understanding user requirements and providing answers in natural, easy-to-understand language. This paper presents directions for the development and use of large-language-model-based virtual assistants, focusing on their ability to understand and interact with the geospatial domain through an LLM API. Geospatial modeling contributes significantly to the automation of public services, but access to this technology is often limited by technical expertise or dedicated software programs. The development of AI-based virtual assistants removes these barriers, facilitating access, reducing time and ensuring transparency and accuracy of information. The proposed approach is implemented using a commercial large language model API, integrated with domain-specific geospatial functions and authoritative spatial databases. This study highlights practical examples of virtual assistants capable of understanding the geospatial field and contributing to the optimization and automation of public services in the country. In addition, the paper presents comparative analyses, challenges encountered and potential directions for future research. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article

In the fields of geographic information science and location-based services, the fusion of multisource Point-of-Interest (POI) data is of remarkable importance but faces several challenges. Existing matching methods, including those based on single non-spatial attributes, single spatial geometric features, and traditional hybrid methods with fixed rules, suffer from limitations such as reliance on a single feature and inadequate consideration of spatial context. This study takes Dongcheng District, Beijing, as the research area and proposes a POI-matching method based on multi-feature value calculation and a deep neural network (DNN) model. The method comprehensively incorporates multidimensional features such as names, addresses, and spatial distances. Additionally, the approach also incorporates an improved multilevel name association strategy, an address similarity calculation using weighted edit distance, and a spatial distance model that accounts for spatial density and regional functional types. Furthermore, the method utilizes a deep learning model to automatically learn POI entity features and optimize the matching rules. Experimental results show that the precision, recall, and F1 value of the proposed method achieved 97.2%, 97.0%, and 0.971, respectively, notably outperforming traditional methods. Overall, this method provides an efficient and reliable solution for geospatial data integration and POI applications, and offers strong support for GIS optimization, smart city construction, and scientific urban/town planning. However, this method still has room for improvement in terms of data source quality and algorithm optimization. Full article