Search Results (947)

The rapid expansion of telecommunication infrastructure in developing countries has increased the demand for sustainable strategies to deploy field engineers in tower maintenance operations. Traditional approaches often neglect spatial factors, resulting in inefficient workforce allocation, excessive travel, and higher carbon emissions. This study develops an applied geospatial deployment framework that integrates spatial analysis with sustainable supply chain management (SSCM) principles to support operational decision-making in resource-constrained telecommunication maintenance environments. Using publicly available tools, tower and homebase coordinates were mapped and analyzed through Haversine-based geodesic distance calculations, with a comparative assessment against Euclidean approximation, while incorporating operational constraints such as service time per tower, available personnel, and work-hour limitations. The results indicate that the existing two-homebase deployment strategy leads to unbalanced workloads and unnecessary travel distances. By introducing a cluster-based restructuring using k-means to identify four sub-homebases, the proposed approach reduces total round-trip travel distance from 9120 km to 5913 km per maintenance cycle, representing a 35.2% reduction. This distance reduction corresponds to an estimated saving of approximately 593 kg of CO₂ emissions per maintenance cycle, representing an operational-scale reduction in travel-related emissions based on distance-derived fuel consumption modeling and assuming typical fuel efficiency for service vehicles. In addition, the optimized spatial configuration enables a more equitable distribution of engineers and reduces travel-related fatigue. These findings demonstrate the value of integrating geospatial optimization with sustainable supply chain management by aligning operational efficiency with quantifiable environmental and social sustainability outcomes. The proposed framework offers a replicable, low-cost, and data-driven solution for telecommunication infrastructure providers seeking to enhance the sustainability of field service operations in resource-constrained environments. Full article

Monitoring heritage landscapes is essential for evaluating long-term ecological integrity, mitigating environmental risks, and supporting sustainable heritage management. This study investigates land cover transformation and ecological environment quality in the Jingmai Mountain Cultural Landscape Heritage Area, a UNESCO World Heritage Site, using high-resolution satellite imagery from 2013 and 2023 and geospatial analysis tools (ENVI 5.3 and ArcGIS 10.8). Supervised classification using the maximum likelihood algorithm was employed to detect land use and land cover changes, and a quantitative ecological environment quality index based on land use areas and ecological coefficients was used to assess regional ecological quality. Land cover dynamics, heritage element shifts, and ecological quality variations before and after the site’s inscription were analyzed. The results indicate that core landscape structures remained relatively stable in both the construction control area and the core application zone. In the construction control area, land cover changes totaled 32.28 km², with the most significant transformations occurring in forested areas (36%), followed by cultivated lands (19%). In the application zone, total land cover change reached 10.99 km², primarily involving cultivated lands (33%) and built-up areas (27%). Ecological environment quality indices exhibited a slight positive trend, increasing from 0.4476 to 0.4512 in the construction control area and from 0.2449 to 0.2521 in the application zone between 2013 and 2023. This study provides a decade-long spatial assessment of land use transitions in a UNESCO cultural landscape and proposes a transferable framework for integrating ecological quality evaluation into heritage landscape monitoring. The findings offer evidence-based insights into heritage conservation and rural development planning and support the implementation of sustainable landscape management strategies aligned with national policies and the Sustainable Development Goals. Full article

Semantic segmentation of remote sensing imagery is crucial for applications such as land resource management and urban planning, yet it remains challenging due to low intra-class variation, ambiguous boundaries, and the coexistence of multi-scale geospatial features. To tackle these issues, we propose GS-USTNet, a novel architecture that enhances both feature representation and boundary recovery. First, we introduce a Global–Local Adaptive Convolution (GLAConv) module that dynamically fuses global contextual cues with local responses to generate content-aware convolutional weights, thereby improving feature discriminability. Second, we design a Skip-Guided Attention (SGA) mechanism that leverages spatial–channel joint attention to guide the decoder, effectively mitigating attention dispersion in complex scenes or under class imbalance and significantly sharpening object boundaries. Built upon the efficient USTNet framework, our model achieves substantial performance gains without compromising computational efficiency. Extensive experiments on benchmark datasets demonstrate that GS-USTNet achieves consistent improvements over the original USTNet, with gains of approximately 3.5% in overall accuracy and 6.0% in F1-score across datasets. Ablation studies further confirm the effectiveness of the proposed GLAConv and SGA modules. This work provides an efficient and robust approach for fine-grained semantic segmentation of high-resolution remote sensing imagery. Full article

As a result of China’s rapid urbanization, new urban districts are characterized by a superblock development paradigm that contrasts sharply with core urban areas, where service facilities remain largely congruent with the population distribution. This planning approach has resulted in a pronounced spatial mismatch, with an intensive concentration of public service facilities within commercial cores and a critical lack of facilities proximate to high-density residential clusters. Within the framework of the 15 min community life circle policy, evaluating and optimizing these configurations is imperative for mitigating such structural imbalances. Using Xi’an’s Qujiang New District as a representative empirical case, this study integrates Point of Interest (POI) geospatial data with 330 resident behavioral questionnaires to assess facility distribution and utilization patterns. The findings reveal a distinct spatial pattern of core–periphery polarization, which is significantly influenced by cultural landscapes and commercial land values. Furthermore, the utilization patterns differ markedly across age groups. The reliance of young and middle-aged groups on digital life circles should be viewed not only as a lifestyle preference but also as an adaptation to mitigate physical facility deficits. While digital services compensate for physical facility shortages, they mask the actual lack of community spaces. This further disadvantages older adults, who still rely heavily on walking to access daily services. Addressing the unique characteristics of new urban districts, this study proposes a synergistic physical–digital dual-tier system in which physical infrastructure safeguards the equity baseline, while digital platforms enhance operational efficiency, providing a scientific basis for constructing age-friendly communities. Full article

The global expansion of continuous GNSS networks has generated large-scale spatiotemporal datasets whose analysis requires robust mathematical and statistical tools. This study introduces a geospatial, multivariate statistical framework for classifying 21,548 GNSS stations from the University of Nevada repository. The methodology integrates harmonic regression, stochastic noise modeling, quality assessment, and slope estimation into a unified feature space suitable for high-dimensional analysis. Using unsupervised learning clustering computed with our custom-developed code, based entirely on free and open-source software, we identify homogeneous station groups that reflect dominant signal properties—periodicity, noise structure, data quality, and long-term velocity—together with their spatial context. The resulting clusters exhibit strong mathematical coherence and reveal continental-scale patterns driven by seasonal forcing, tectonic regime, climatic variability, and monument stability. By grouping stations with similar statistical behavior, the proposed framework improves reference-site selection, enhances deformation-field interpretation, and supports the detection of anomalous or hazard-related behavior. Overall, this approach provides a scalable, data-driven mathematical tool for analyzing complex spatiotemporal signals and contributes to more reliable deformation modeling and environmental risk assessment. 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

Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha⁻¹) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha⁻¹ in HH areas versus 17.64 t C ha⁻¹ in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls. Full article

Rapid urbanization in coastal regions presents complex challenges for environmental management and public safety. Accurate, high-resolution wind field monitoring is critical for urban disaster mitigation, infrastructure resilience, and pollutant dispersion analysis in these densely populated areas. However, utilizing massive multi-source satellite remote sensing data for precise prediction remains difficult due to the spatiotemporal heterogeneity caused by the land–sea interface. To address this, this study proposes a novel lightweight Geospatial Artificial Intelligence (GeoAI) framework (DA-DSC-UNet) designed to predict wind fields in coastal urban environments (e.g., Fujian, China). We constructed a dataset by integrating multi-source satellite scatterometer products (including Advanced Scatterometer (ASCAT), Fengyun-3E (FY-3E), and Quick Scatterometer (QuickSCAT)) and buoy observations. The framework employs a UNet architecture enhanced with dual attention mechanisms (Efficient Channel Attention (ECA) and Convolutional Block Attention Module (CBAM)) to adaptively extract features from remote sensing signals, focusing on critical spatial regions like urban coastlines. Additionally, depthwise separable convolutions (DSCs) are introduced to ensure the model is lightweight and efficient for potential deployment in urban monitoring systems. Results demonstrate that our approach significantly outperforms existing deep learning models (reducing Mean Absolute Error (MAE) by 14–25.8%) and exhibits exceptional robustness against observational noise. This work demonstrates the potential of deep learning in enhancing the value of remote sensing data for urban resilience, sustainable development (SDG 11), and environmental monitoring in complex coastal zones. Full article

Accurate crop identification in plateau mountainous regions is essential for food security, yet geospatial non-stationarity and topography-induced spectral paradoxes often compromise global model performance due to a “homogenization constraint.” This study developed an adaptive local modeling framework that partitions the landscape into biophysically homogeneous subregions using the Spectral Angle Mapper (SAM), effectively isolating terrain-induced illumination variance from intrinsic spectral responses. Independent local classifiers were coupled with SHAP and GAMs to interpret the resulting spatial variations in feature contributions. Results demonstrate that: (1) the partitioned local models significantly outperform the global baseline (OA = 93.1%, Kappa = 0.919) by mitigating the suppression of local signals inherent in global datasets; (2) the framework captures a mechanistic shift in feature importance, transitioning from a strong “Hydro-Topographic” coupling in highlands (Interaction Strength > 0.15) to “Spectral-Texture” complementarity in plains; and (3) major crop distributions are governed by quantifiable biophysical thresholds—such as a <28.5 °C thermal limit for maize and a >971 mm precipitation boundary for rice—which exhibit consistency with regional agrometeorological principles. These findings suggest that integrating adaptive partitioning with interpretable local modeling transforms geospatial non-stationarity from a source of classification error into explicit, zone-specific decision rules, providing a robust and scientifically grounded solution for precision agriculture in heterogeneous terrains. Full article

Assessing urban resilience under compound shocks requires observable and comparable process evidence that can inform resilient land governance and cross-jurisdiction planning. Using China’s Pilot Free Trade Zones (PFTZs) as a staged institutional setting, this research examines whether institutional exposure is associated with deviation–recovery trajectories of urban activity during the 2020 COVID-19 shock and whether these associations propagate through spatial spillovers with an identifiable scale profile. Institutional exposure is operationalized by the prefecture-level cities actually covered by PFTZ functional areas. With harmonized administrative boundaries, we construct an annual city-level VIIRS nighttime light (NTL) series for 2013–2024 and treat NTL as an activity-change signal rather than a direct proxy for output. We trace shock deviation in 2020 and subsequent recovery via staged differencing. Spatial interaction frictions are represented by least-cost path distance (LCPD) derived from a multi-source cost surface, which is used to build a gravity-based spatial weight matrix. Estimation relies on the Spatial Durbin Model (SDM), with LeSage–Pace impact decomposition to distinguish direct and spillover effects, complemented by distance-threshold diagnostics to map attenuation patterns. Results indicate persistent clustering within the PFTZ-related urban system. The shock year is characterized by compressed connectivity and fragmented brightening, whereas recovery proceeds in a layered manner with earlier core repair, partial corridor reconnection, and weaker adjustment at the periphery. Spatial dependence in activity change is statistically significant. Associations linked to institutional exposure are realized primarily locally, while structural and scale conditions more readily operate through spatial externalities. Spillovers are most detectable at meso-scales and attenuate gradually across distance thresholds. Overall, the integrated earth-observation and spatial-econometric framework provides replicable geospatial evidence to support resilient land governance and regional coordination under common shocks. Full article

The limited availability of structured and consistent health-facility information poses challenges for assessing service accessibility and quality in rapidly growing cities, particularly in the Middle East. Although digital map platforms provide extensive public data, such information is often fragmented and not directly suitable for systematic spatial analysis. This study presents GeoJed, a framework designed to automate the collection, organisation, and spatial analysis of healthcare facility information from digital map platforms. The framework is demonstrated through a case study in Jeddah, Saudi Arabia, highlighting its applicability for large-scale and reproducible spatial analysis of healthcare services. Using the resulting GeoJedHF dataset, a baseline analysis was conducted to illustrate the analytical value of the collected data, including the construction of an initial Patient Satisfaction Index (PSI) that integrates service availability with user-reported quality indicators derived from a multilingual sentiment model (XLM-RoBERTa). The results reveal clear spatial variations between districts in both facility distribution and perceived service quality. Overall, GeoJed establishes a reusable and extensible process for facility-level spatial data acquisition and analysis, with potential applications in accessibility assessment, urban planning, and service evaluation. Full article

Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO₂ emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO₂ than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies. Full article

This study applies an integrated analytical framework combining GeoDetector and multiscale geographically weighted regression (MGWR) to examine how the spatial distribution of cultural heritage values in the Chungcheong region of South Korea (Chungcheongnam-do and Chungcheongbuk-do) relates to regional socio-spatial contexts. Using the Korea Heritage Service’s heritage basic survey data (coordinates, attributes, and value assessments), we aggregated heritage value scores to a 1 km grid and modeled six value dimensions—historical, artistic, academic, social, rarity, and conservation—as separate dependent variables. We then integrated socio-spatial indicators derived from statistical grid maps published by the National Geographic Information Institute (official land price, building density, green space, road accessibility, total population, working-age population share, and aging rate). GeoDetector was first used to identify key determinants and interaction effects by value dimension, and MGWR was then used to estimate local effect heterogeneity and variable-specific operating scales. Results show that heritage values are better explained by multi-factor configurations—urbanization, land value, green space, accessibility, and demographic structure—whose importance varies by value dimension, and that the same factor can exert different directions and strengths across local contexts. By linking “what matters” (key determinants) with “where and at what scale it matters” (local effects and bandwidths), this study provides quantitative evidence to support place-based conservation and utilization strategies. The proposed GeoDetector–MGWR framework is transferable to other regions where spatial heritage inventories and comparable socio-spatial indicators are available. Full article

Building extraction from high-resolution remote sensing imagery holds significant value for urban planning, disaster assessment, and geospatial analysis. However, current semantic segmentation models still face limitations when handling complex scenarios characterized by diverse building morphologies, significant scale variations, and blurred boundaries. To address the challenges of insufficient long-range dependency modeling, suboptimal multi-scale feature representation, and weak spatial adaptability, this paper proposes a building extraction network that integrates multi-scale sequence modeling with spatial adaptive enhancement. Adopting UPerNet (equipped with ConvNeXt-Tiny) as the baseline framework, the proposed method introduces a dedicated PyramidSSM-based neck (PyramidSSMNeck) as the primary design for multi-scale feature alignment and fusion, and further integrates three enhancement components (S6 (SSM-based), LSKNet, and SAFM) that provide additional improvements mainly reflected in boundary delineation. Specifically, PyramidSSMNeck performs structured cross-scale feature projection, alignment, and aggregation to strengthen multi-scale representation; S6 enhances long-range contextual modeling, LSKNet adaptively adjusts spatial receptive fields to accommodate scale variations, and SAFM modulates feature responses with spatial cues to refine boundaries and fine details—forming a unified framework in which PyramidSSMNeck primarily drives multi-scale alignment and fusion, while S6, LSKNet, and SAFM further enhance long-range context modeling and spatial adaptivity, mainly benefiting boundary preservation and fine-detail integrity. Experiments were conducted on the WHU Building, INRIA, and a self-constructed Ganzhou urban dataset, and the results indicate that the proposed method achieved IoU scores of 91.29%, 81.96%, and 88.18% across the three datasets, outperforming the baseline UPerNet (ConvNeXt-Tiny) by 2.37%, 0.88%, and 3.68%, respectively, with F1-scores consistently exceeding 90%. Importantly, ablation results indicate that the majority of region-level gains (IoU/F1) come from PyramidSSMNeck, whereas the additional modules contribute more prominently to boundary quality, yielding a Boundary IoU increase from 63.29% to 65.63% (+2.34) from the neck-only setting to the full model. Visualization results further support the method’s advantages in boundary preservation and detail integrity, and additional cross-domain transfer experiments (zero-shot and few-shot from WHU to Ganzhou) suggest improved robustness under domain shift. Full article

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay operations, thereby posing systemic threats to regional grid stability. To enhance wildfire early-warning efficacy for grid security, this study formulates wildfire early warning for power transmission corridors as a regression-based risk prediction problem and proposes a hierarchical “global screening–local refinement” risk assessment framework. The primary contribution of this study lies in the integration of a machine-learning-based global wildfire risk screening model with tower-level spatial refinement using geographically weighted regression (GWR), enabling coordinated global–local wildfire risk characterization along power transmission corridors The framework employs a predictive model built on a Gradient Boosting Decision Tree algorithm, integrating geospatial and statistical analyses. A global risk model, utilizing historical data from the Himawari-8 satellite alongside meteorological, topographic, and anthropogenic variables, produces a composite risk index. This index is spatially interpolated via Kriging to generate stratified wildfire risk maps for broad-area assessment. For precise corridor-level analysis, these Globally Projected Risk Indices, along with localized terrain features, inter-tower clearance distances, and proximity to historical ignition points, are incorporated into a Geographically Weighted Regression model. This yields a spatially calibrated wildfire risk index along critical routes. The results show that the GBDT-based model achieved the best predictive performance among the evaluated regression models, with an R² of 0.626 and a mean squared error of 0.178. This approach offers a scientifically robust and operationally viable reference for wildfire prevention strategies in power line maintenance. Full article