Search Results (660)

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data. Full article

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

Urban growth and environmental pressures in rapidly transforming cities require innovative governance tools that integrate advanced technologies with institutional assessment. This study develops and applies a strategic integration framework that combines spatial analysis, Convolutional Neural Networks (CNNs)-based land-use classification, SHAP-based feature attribution, and stakeholder interviews to evaluate Yerevan, Armenia, as a case of a mid-income city facing accelerated urbanization. The case selection is justified by Yerevan’s rapid built-up expansion, fragmented green areas, and institutional challenges in aligning urban development with sustainability goals. The CNN model achieved 92.4% accuracy in land-use classification, and projections under a business-as-usual scenario indicate a 12.8% increase in built-up areas and a 6.5% decline in green zones by 2030. SHAP analysis identified land surface temperature and NDVI as the most influential predictors, while governance interviews highlighted gaps in regulatory support and technical capacity. The proposed framework advances the literature by integrating AI-driven geospatial analysis with qualitative governance assessment, providing actionable insights for urban policymakers. Findings underscore the potential of combining machine learning, geospatial technologies, and institutional diagnostics to guide smart city planning in transition economies. Full article

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial networks using 8-adjacency lattices. Our unified framework integrates OOP with stochastic geometry, modeling obstacle placement via Strauss (regular) and Matérn (clustered) processes, and evaluates traversal using the Reset Disambiguation algorithm. Through extensive Monte Carlo experiments, we show that traversal cost increases by up to 40% under strongly regular placements, while clustered configurations can decrease traversal costs by as much as 25% by leaving navigable corridors compared to uniform random layouts. In mixed (with both true and false obstacles) scenarios, increasing the proportion of true obstacles from 30% to 70% nearly doubles the traversal cost. These findings are further supported by statistical analysis and stochastic ordering, providing rigorous insights into how spatial patterns and obstacle compositions influence navigation under uncertainty. 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

Ecotourism is vital for harmonious human–nature coexistence in national parks, making the quantification of its spatial suitability an urgent scientific priority. This study took the Yellow River Estuary National Park (YRENP) as the study area and constructed a multi-criteria evaluation model by interpreting the relationship between Ecotourism Environmental Carrying Capacity (EECC) and Ecotourism Development Suitability (EDS), addressing the critical gap in the integrated land–sea ecotourism suitability assessment for coastal national parks, using the Analytic Hierarchy Process (AHP) to determine indicator weights and ArcGIS for spatial visualization. Multi-source geospatial data, including land use, NDVI, DEM, and socio-economic datasets, were integrated. The results revealed the following: (1) Overall moderate EECC levels with stronger terrestrial capacity contrast with weaker marine capacity—high-carrying zones being limited to nearshore areas; (2) The overall EDS level was favorable, where southern section significantly outperformed northern zones, forming concentrated high-suitability clusters encircling lower-suitability areas; (3) Marine EDS slightly exceeds terrestrial suitability, with optimal coastal zones transitioning landward toward progressively higher suitability. This research provided a replicable methodology for ecotourism suitability assessment in coastal protected areas and supported sustainable spatial planning in land–sea integrated national parks. Full article

Southern Kazakhstan, particularly the Zhambyl Region, is facing increasing groundwater stress due to climate change, degradation of irrigation infrastructure, and unsustainable water use. Despite substantial renewable groundwater reserves (8.33 km³/year), irrigation still relies on ephemeral surface flow. This study delineates priority zones for Managed Aquifer Recharge (MAR) using a GIS-based Multi-Criteria Decision Analysis framework integrated with the Analytic Hierarchy Process (AHP). Nine hydrogeological criteria were incorporated: shallow aquifer depth, groundwater salinity, precipitation, terrain slope, soil texture, land use/land cover, Normalized Difference Vegetation Index (NDVI), drainage density, and lineament density. Each parameter was normalized to a five-class suitability scale and weighted through expert-informed pairwise comparisons. The MAR suitability map identifies about 19% of the region (27,060 km²) as highly favorable for implementation. Field investigations at eleven groundwater sites in 2024 corroborate model results, providing aquifer depth, quality, and infiltration data. The most suitable areas are concentrated on Quaternary alluvial–proluvial fans near the Kyrgyz Alatau foothills and the Talas-Assa interfluve. Three hydrostratigraphic settings were identified: unconfined alluvial aquifers, Neogene–Quaternary unconsolidated sediments, and fractured Carboniferous carbonates. Recommended MAR methods include infiltration galleries, check dams, and injection wells. The proposed approach, validated through consistency analysis (Consistency Ratio ≤ 0.1), demonstrates the applicability of integrated geospatial and field methods for site-specific MAR planning. Strategic MAR deployment could restore productivity to 37,500 ha of degraded irrigated lands and improve groundwater resilience. These findings provide a practical framework for policymakers and water management authorities to optimize groundwater use and enhance agricultural sustainability under changing climatic conditions. Full article

Fodder maize constitutes a key economic crop in Thailand, particularly in the northeastern region, where it significantly contributes to livestock feed production and local economic development. Nevertheless, the planning and management of cultivation areas remain a major challenge, especially in urban and peri-urban agricultural zones, due to the limited availability of spatial data and suitable analytical frameworks. These difficulties are exacerbated in urban settings, where the complexity of land use patterns and high spectral similarity among land cover types hinder accurate classification. The Google Earth Engine (GEE) platform provides an efficient and scalable solution for geospatial data processing, enabling rapid land use classification and spatiotemporal analysis. This study aims to enhance the classification accuracy of fodder maize cultivation areas in Mueang District, Nakhon Ratchasima Province, Thailand—an area characterized by a heterogeneous mix of urban development and agricultural land use. The research integrates GEE with four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Classification and Regression Trees (CART). Eleven datasets were developed using Sentinel-2 imagery and a combination of biophysical variables, including elevation, slope, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), to classify land use into six categories: fodder maize cultivation, urban and built-up areas, forest, water bodies, paddy fields, and other field crops. Among the 44 classification scenarios evaluated, the highest performance was achieved using Dataset 11—which integrated all spectral and biophysical variables—with the SVM classifier. This model attained an overall accuracy of 97.41% and a Kappa coefficient of 96.97%. Specifically, fodder maize was classified with 100% accuracy in both Producer’s and User’s metrics, as well as a Conditional Kappa of 100%. These findings demonstrate the effectiveness of integrating GEE with machine learning techniques for precise agricultural land classification. This approach also facilitates timely monitoring of land use changes and supports sustainable land management through informed planning, optimized resource allocation, and mitigation of land degradation in urban and peri-urban agricultural landscapes. Full article

Renewable energy expansion requires cost-effective strategies to integrate underutilized biomass resources into energy systems. In Australia, forest residues represent a significant but largely untapped feedstock that could contribute to a more diversified energy portfolio. This study presents an integrated geospatial and optimization decision-support model designed to minimize the total cost of forest biomass-to-bioenergy supply chains through optimal facility selection and network design. The model combined geographic information systems with mixed-integer linear programming to identify the optimal candidate facility sites based on spatial constraints, biomass availability and infrastructure proximity. These inputs then informed an optimization framework that determined the number, size, and geographical distribution of bioenergy plants. The model was applied to a case study in Queensland, Australia, evaluating two strategic scenarios: (i) a biomass-driven approach that maximizes the use of forest residues; (ii) an energydriven approach that aligns facilities with regional energy consumption patterns. Results indicated that increasing the minimum facility size reduced overall costs by capitalizing on economies of scale. Biomass collection accounted for 81%–83% of total supply chain costs (excluding capital installation), emphasizing the need for logistically efficient sourcing strategies. Furthermore, the system exhibited high sensitivity to transportation distance and biomass availability; energy demands exceeding 400 MW resulted in sharply escalating transport expenses. This study provides a scalable, data-driven framework for the strategic planning of forest-based bioenergy systems. It offers actionable insights for policymakers and industry stakeholders to support the development of robust, cost-effective, and sustainable bioenergy supply chains in Australia and other regions with similar biomass resources. Full article

Gulf cities have embarked on ambitious public transport infrastructure initiatives in recent decades to foster more livable and sustainable cities. This investigation explores the interpretations and implementation of Transit-Oriented Development (TOD) principles in two prototypical urban districts: Doha’s West Bay, Qatar, and Riyadh’s King Abdullah Financial District (KAFD), Saudi Arabia. By following a comparative case study approach, the study explores how retrofitted (West Bay) and purpose-built (KAFD) TOD configurations fare regarding land use mix, density, connectivity, transit access, and environmental responsiveness. The comparative methodology was selected to specifically capture the spatial, climatic, and socio-economic complexities of TOD implementation in hyper-arid urban environments. Based on qualitative evidence from stakeholder interviews, spatial assessments, and geospatial indicators—such as metro access buffers, building shape compactness, and TOD proximity classification—the investigation reflects both common challenges and localized adaptations in hot-desert Urbanism. It emerges that, while benefiting from integrated planning and multimodal connectivity, KAFD’s pedestrian realm is delimited by climatic constraints and inactive active transport networks. West Bay, on the other hand, features fragmented public spaces and low TOD cohesion because of automotive planning heritages. However, it holds potential for retrofit through infill development and tactical Urbanism. The results provide transferable insights that can inform TOD strategies in other Gulf and international contexts facing similar sustainability and mobility challenges. By finalizing strategic recommendations for urban livability improvement through context-adaptive TOD approaches in Gulf cities, the study contributes to the wider discussion of sustainable Urbanism in rapidly changing environments and supplies a reproducible assessment frame for future TOD planning. This study contributes new knowledge by advancing a context-adaptive TOD framework tailored to the unique conditions of hyper-arid Gulf cities. Full article

As nations worldwide commit to carbon neutrality targets in response to accelerating climate change, the spatial modeling of carbon emissions has emerged as an indispensable tool for policy implementation and assessment. This paper presents a systematic review of the field from bibliometric and methodological perspectives. We synthesize key developments in spatial allocation techniques, data-driven models, and emission characterization methods. A central focus is the transformative role of geospatial big data in improving model accuracy and applicability, particularly how fine-grained, high-resolution modeling enhances the efficacy of emission reduction strategies. Our analysis reveals several key conclusions. First, the literature on carbon emission spatial modeling is expanding rapidly, with a discernible shift in focus from coarse, large-scale assessments toward more granular analyses that are sector-specific, high-resolution, and multidimensional. Second, hybrid models that integrate top-down and bottom-up approaches are now the predominant strategy for enhancing both accuracy and applicability; coupling mechanistic models with machine learning techniques effectively reconcile macro-scale data consistency with micro-scale heterogeneity. Third, the integration of geospatial big data is revolutionizing the field by providing the high-resolution, multidimensional, and dynamic inputs necessary to transition from macro- to micro-scale analysis. This is particularly evident in fine-grained assessments of urban systems—including spatial functions, morphology, and transportation networks—where such data dramatically improve the characterization of emission sources, intensities, and their spatiotemporal heterogeneity. This study ultimately elucidates the critical role of fine-grained modeling in advancing the quantitative understanding of carbon emission drivers, enabling robust scenario simulations for carbon neutrality, and informing effective low-carbon spatial planning. The synthesis presented here aims to provide a firm theoretical and technical foundation to support the ambitious carbon reduction targets set by nations worldwide. Full article

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts. Full article

Unmanned aerial vehicles (UAVs) play an increasingly vital role in wilderness search and rescue (SAR) operations by enhancing situational awareness and extending the capabilities of human teams. Yet, a lack of standardized benchmarks has impeded the systematic evaluation of single- and multi-agent path-planning algorithms. This paper introduces an open-source dataset and evaluation framework to address this gap. The framework comprises 60 geospatial scenarios across four distinct European environments, featuring high-resolution probability maps. We present a lost person probabilistic model derived from statistical models of lost person behavior. We provide a suite of tools for evaluating search paths against four baseline methods: Concentric Circles, Pizza Zigzag, Greedy, and Random Exploration, using three quantitative metrics: Accumulated probability of detection, time-discounted probability of detection, and lost person discovery score. We provide an evaluation framework to facilitate the comparative analysis of single- and multi-agent path-planning algorithms, supporting both the baseline methods presented and custom user-defined path generators. By providing a structured and extensible framework, this work establishes a foundation for the rigorous and reproducible assessment of UAV search strategies in complex wilderness environments. Full article

Flash floods arise from the interaction of rugged topography, short-duration intense rainfall, and rapid flow concentration. Conventional risk mapping often builds empirical indices with expert-assigned weights or trains supervised models on historical event inventories—approaches that degrade in data-scarce regions. We propose a fully data-driven, unsupervised Geographic Information System (GIS) framework based on fractional order k-means, which clusters multi-dimensional geospatial features without labeled flood records. Five raster layers—elevation, slope, aspect, 24 h maximum rainfall, and distance to the nearest stream—are normalized into a feature vector for each 30 m × 30 m grid cell. In a province-scale case study of Zhejiang, China, the resulting risk map aligns strongly with the observations: 95% of 1643 documented flash flood sites over the past 60 years fall within the combined high- and medium-risk zones, and 65% lie inside the high-risk class. These outcomes indicate that the fractional order distance metric captures physically realistic hazard gradients while remaining label-free. Because the workflow uses commonly available GIS inputs and open-source tooling, it is computationally efficient, reproducible, and readily transferable to other mountainous, data-poor settings. Beyond reducing subjective weighting inherent in index methods and the data demands of supervised learning, the framework offers a pragmatic baseline for regional planning and early-stage screening. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article