Search Results (482)

Oil spills are severe environmental disasters that cause long-lasting damage to marine ecosystems and impose significant economic costs, underscoring the urgent need for efficient detection and monitoring technologies. Conventional field-based observation methods, while valuable, are constrained by limited spatial coverage, high costs, and labor-intensive processes, making them impractical for large-scale or rapid-response applications. To overcome these challenges, satellite remote sensing has been used as an effective alternative for oil spill monitoring. In particular, the advent of Google Earth Engine (GEE), a cloud-based geospatial platform, has transformed oil spill research by enabling scalable management and analysis of large satellite remote sensing datasets. This review synthesizes studies employing GEE for oil spill detection, across marine environments and interconnected aquatic systems, focusing on methodologies based on optical imagery and synthetic aperture radar data and approaches that integrate machine learning techniques. The analysis underscores that GEE enhances oil spill monitoring by facilitating rapid data processing, supporting reproducible workflows, and expanding access to multi-source satellite data. Furthermore, this review highlights the necessity of incorporating very-high-resolution satellite data and achieving tighter integration of external deep learning framework within GEE to improve detection accuracy and the operational applicability in complex marine and coastal contexts. Full article

Urban areas require context-specific bioenergy solutions to advance toward circular and sustainable energy systems. In Bogotá, urban pruning and grass-cutting residues constitute a relatively stable biomass stream; however, the absence of district-scale valorization infrastructure leads to their direct disposal in landfill. This study develops and applies a GIS-based planning methodology to support the territorial design of a small-scale anaerobic digestion plant using urban green waste. In this study, “small-scale” is understood as an early-stage urban facility concept compatible with the available pruning stream of approximately 1200–1300 t/month of valorizable biomass, corresponding only to an order-of-magnitude energy range of a few hundred kW_e/kW_t, rather than to a final engineering design. The approach integrates official geospatial data with logistical, environmental, and institutional criteria to characterize biomass availability and evaluate location alternatives under real urban constraints. A continuous location model based on the Weber problem is first applied to estimate a theoretical lower bound of spatial effort, using public schools weighted by enrollment as a proxy for sensitive urban demand. Subsequently, a GIS-assisted Analytic Hierarchy Process (AHP) is implemented to incorporate environmental exclusions, territorial compatibility, and the operational structure of exclusive waste service areas. Results show that the optimal geometric location diverges from the territorially feasible alternative once environmental restrictions and biomass supply coherence are explicitly considered. The findings highlight that urban bioenergy infrastructure planning is governed less by pure spatial efficiency than by the integration of supply, demand, and institutional constraints. The proposed methodology provides a reproducible decision-support tool for urban bioenergy planning and contributes to sustainable waste management, circular economy strategies, and local energy resilience in cities of the Global South. Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article

The rapid growth of geospatial data and advances in artificial intelligence (AI) have driven GeoAI’s rise as a key paradigm in urban analytics. GeoAI methods support spatial planning, risk assessment, and policymaking in cities facing climate change, socio-economic disparities, and environmental challenges. Recent research highlights improvements in methodology, decision-making support, and impacts on resilience, social inclusion, and fair governance. However, this review also addresses ongoing issues such as data access, model transparency, ethical concerns, and the varying relevance across Global North and Global South contexts. It explores opportunities to use GeoAI to enhance climate resilience, alleviate poverty, foster inclusive urban strategies, and develop better cities, while suggesting future research to ensure that GeoAI advances are fair, transparent, and aligned with urban policy goals. Full article

Recent large-scale renewable energy projects, such as the Luzia Solar and Chafariz Wind energy plants in Santa Luzia, Paraíba, Brazil, raised environmental concerns due to their impact on vegetation cover and landscape structure. This study used geospatial technologies to evaluate changes in tree cover and landscape configuration resulting from the installation of these projects. Sentinel-2 imagery processed in Google Earth Engine generated NDVI, SAVI, NDWIveg, and LAI vegetation index data for the dry and rainy seasons of the six years between 2019 and 2024. With these vegetation index values and considering MapBiomas (version 8.0) and FRAGSTATS software (version 4.2), we analyzed the changes in land use and vegetation cover of Santa Luzia municipality during this six-year period. Land use and vegetation cover remained stable from 2019 to 2020 (before the installation of the energy plants), characterized by an NDVI value of 0.60, while tree cover decreased in the following four years, during or after the installation of the energy plants, as indicated by the consistent decreases in NDVI and NDWIveg values. Grassland class areas declined from 41.80% (18,434.59 ha) in 2019, to 34.36% (15,151.22 ha) in 2023, while non-vegetated areas increased by 148%. Landscape metrics showed increased fragmentation, with patch density rising from 3.31 to 3.88 patches/100 ha and core area decreasing from 3045.60 ha to 1395.01 ha. These data demonstrated measurable ecological impacts linked to the infra-structure built to run the two solar and wind energy plants in the semi-arid region of Santa Luzia, Paraíba, Brazil. Full article

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

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

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

Since children spend a significant portion of their developmental years in educational settings, the environmental quality of these institutions—specifically, the extent to which they expose their occupants to green space and heat stress—is a critical determinant of well-being and academic performance. This study assesses the green environmental quality of 121 educational institutions (kindergartens, and elementary and secondary schools) in Debrecen, Hungary. The main objective of the research is to identify educational institutions that require immediate intervention to address their lack of green spaces, improve the green environment, and mitigate the urban heat island (UHI) effect. A further aim of the study is to understand how different urban planning practices over the past century have led to the current situation. Therefore, we utilized high-resolution geospatial data (specifically, WorldView-2 imagery) to classify schoolyard vegetation; Landsat data to derive Land Surface Temperature (LST); and the Hoover index to quantify institutions’ spatial concentration. We developed a composite indicator to categorize green environmental quality and heat stress exposure. Our results reveal deep spatial and institutional inequalities. 47.5% of students attend institutions with low environmental quality. While kindergartens typically offer green-rich environments, secondary schools with significant student populations—which are primarily concentrated in the dense historical downtown—are trapped in “grey” zones possessing poor environmental quality. Furthermore, we identify a “green paradox” in socialist housing estates: despite abundant surrounding greenery, schools here record high LST values due to the heat-trapping morphology of vertical concrete structures. The study also highlights institutional maladaptation, such as converting schoolyards into parking lots and using rubber pavements for safety reasons, which contributes to the deterioration of environmental quality. We conclude that current urban planning and school architecture must shift paradigms, treating schoolyards as integral components of the public green infrastructure network through climate-adaptive design. In addition, stakeholders should develop the green environment of educational institutions comprehensively, taking into account both on-site and surrounding green spaces. Full article

Environmental degradation in Iraq is a critical issue that requires strong monitoring. One indication of land degradation is a decrease in or loss of vegetation cover. This study examines changes in vegetation and productivity in the Thi-Qar region from 2001 to 2022, using the normalized difference vegetation index (NDVI) and net primary production (NPP), and their response to climatic and hydrological factors. To address the gap in assessments that simultaneously quantify the influence of streamflow, rainfall, and temperature across distinct land cover classes in arid and semi-arid regions, we developed a replicable multi-source geospatial framework. We used MODIS data within the Google Earth Engine platform to perform spatiotemporal analysis. We applied models to detect NDVI trends on a pixel-by-pixel basis. This study provides the first integrated, data-driven assessment of vegetation sensitivity to streamflow versus climate in the Thi-Qar Governorate using a harmonized multi-source dataset. This combines the FAO WaPOR NPP dataset with hydrological (streamflow) and climatic (CHIRPS rainfall, MODIS LST) variables within an analytical workflow to extract anthropogenic water management from climatic drivers. The results showed variations in the NDVI and productivity in the southern and southwestern regions, indicating areas of both degradation and improvement. The analysis found that 12% of the study area showed improvement, while 56.5% of the area showed degradation. Additionally, we classified the study area as either vegetation (cropland) or non-vegetation (fallow arable land, bare areas, and sand dunes). A multiple regression model was then applied to these categories to examine the relationships between streamflow, precipitation, land surface temperature (LST), and the NDVI. The multiple regression for the entire region showed that these factors explained 45.1% of NDVI variation, with streamflow being the most significant positive driver (p < 0.001). The result showed that the NDVI in cropland and arable land was strongly positively correlated with both precipitation and streamflow (R = 0.78, R = 0.75). In contrast, bare land and dunes showed weaker relationships (R = 0.26 and 0.51, respectively). Of these factors, streamflow had the most significant influence in explaining vegetation change (partial correlation p = 0.53), indicating the importance of human management in addition to climate. Full article

The fusion of Global Navigation Satellite System (GNSS) and Light Detection and Ranging (LiDAR) sensors has emerged as a critical research area for high-precision navigation and mapping applications. While GNSS provides absolute positioning, it is susceptible to multipath errors, signal occlusions, and atmospheric disturbances. LiDAR, on the other hand, offers high-resolution environmental perception but lacks absolute localization and is sensitive to sensor noise and drift over time. To address these limitations, robust sensor fusion architectures are necessary to improve positioning accuracy, reliability, and robustness in diverse environments. This research focuses on the systematic modeling of GNSS and LiDAR errors to enhance sensor fusion performance. A key aspect of this work is the design of fusion architectures that optimize trade-offs between accuracy, environmental-dependency, and robustness to sensor failures. To this end, this research investigates trajectory alignment, geometric similarity, and sensor signal dropouts. Various fusion strategies, including tightly coupled and loosely coupled approaches, are explored to evaluate their effectiveness under different operational conditions. Simulation-based evaluation is a core component of this study, enabling controlled analysis of sensor errors, fusion methodologies, and performance metrics. A custom Blender-based simulation framework has been developed to facilitate reproducible experiments and allow for the benchmarking of different fusion strategies. By systematically analyzing fusion performance in terms of accuracy, consistency, and computational cost, this work aims to provide valuable insights into the optimal integration of GNSS and LiDAR for real-world applications. The simulation framework generates a reusable output format in order to demonstrate the flexibility of this methodology by running a selected fusion approach on real data (Sim2Real). The proposed framework and findings contribute to the research community by providing tools and methodologies for evaluating sensor fusion strategies, fostering advancements in precise and resilient localization solutions for autonomous systems, robotics, and geospatial applications in challenging environments. Full article

Background: Freight allocation is a vital decision in distribution logistics to minimize costs and gain environmental benefits. In this paper, we address the problem of freight allocation optimization on an HSR intermodal network with application for the Ottawa–Quebec City corridor where the HSR system will be constructed. Methods: We develop a novel allocation method in which GNNs encode the intermodal network topology and spatial features, while RL agents learn adaptive freight routing policies through reward optimization, which is enhanced by fractal accessibility metrics for spatial connectivity and MCDM for balancing cost, emissions, and service objectives as well as optimizing dynamic freight flows. The model incorporates geospatial data (population, distance), operational factors (demand, costs), and environmental or policy considerations. Addressing the gap in dynamic, multi-criteria cold-climate HSR freight allocation models for North America, we test our framework on the Ottawa–Quebec corridor. Results: The result shows that compared to traditional methods, the five-hub configuration reduces costs by 15–22% and emissions by 20–28%, while the 11-hub model maintains 94%+ service coverage with an 8–12% efficiency trade-off. Conclusions: The conclusion indicates that the HSR intermodal network is more efficient than road only. Sensitivity analysis highlights that key allocation offers policymakers and logistics planners actionable insights for balancing efficiency and accessibility in HSR freight networks. Full article

Air pollution from industrial point sources remains a major concern in urban environments, highlighting the need for accessible tools that support both education and preliminary environmental assessment. This study presents the development and intercomparison of an open-source, QGIS-based geospatial model for simulating atmospheric pollutant dispersion from fixed point sources using the Gaussian plume formulation. The model integrates emission parameters, meteorological conditions, and terrain data within a fully spatial workflow implemented through the QGIS graphical modeler, enabling the generation of ground-level concentration fields without advanced programming expertise. Dispersion is simulated with and without inclusion of a ground reflection term, allowing comparative analysis of boundary condition effects. The model was applied to a representative urban industrial source at the National University of Science and Technology POLITEHNICA Bucharest, using CO₂ emissions treated as a passive tracer. Model outputs were evaluated through descriptive statistics and quantitative comparison with two established open-source Gaussian plume implementations developed in Python. Ground reflection leads to an increase of approximately 60% in modeled near-surface concentrations, particularly in the upper tail of the distribution, underscoring its importance for screening-level exposure assessment. The proposed model provides a transparent, reproducible, and user-friendly framework suitable for teaching activities, rapid screening analyses, and exploratory air quality assessments. Full article

The widespread application of glyphosate—the world’s most used herbicide—presents a significant environmental challenge due to its persistence and mobility within interconnected land–soil–water systems. This study addresses the limitations of traditional, discrete water monitoring by developing a predictive framework for glyphosate and its primary metabolite, aminomethylphosphonic acid (AMPA), in the agricultural context of Apulia, Southern Italy. The methodology integrates high-sensitivity analytical chemistry with advanced spatial intelligence. Water samples were analyzed using an optimized UHPLC–MS/MS framework with pre-column derivatization (FMOC-Cl), achieving an ultra-trace Limit of Quantification (LOQ) of 0.025 μg/L. To transition from point data to continuous spatial profiles, a hybrid Machine Learning (ML) architecture was implemented. The model utilized a suite of geospatial predictors, including land use (Corine Land Cover), Digital Elevation Models (DEMs), and slope characteristics extracted from river offset lines. A dual-modeling strategy was employed: Global Models (Random Forest, Gradient Boosting, and KNN) for regional trends and Individual Models for river segments exhibiting sufficient internal variability. Analytical findings (2018–2024) revealed that AMPA consistently exhibited higher mean concentrations than glyphosate, reaching peaks of 9.27 μg/L. This trend is primarily attributed to its superior environmental persistence and a half-life of up to 240 days, compared to the parent compound. Spatiotemporal analysis identified critical peaks in the second quarter for glyphosate and extreme surges in the fourth quarter for AMPA, particularly in the Cervaro basin. The Random Forest Regressor emerged as the most robust predictive tool, achieving a coefficient of determination (R²) of approximately 0.68 at the global scale and up to 0.75 for localized models where data density was sufficient. The integration of ML frameworks allows for the identification of contamination “micro-hotspots” and the mapping of probabilistic pollutant distribution along entire river reaches without additional sampling costs. This high-fidelity diagnostic tool provides a cost-effective strategy for environmental agencies to implement targeted mitigation and proactive water resource protection in Mediterranean agroecosystems. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article