Search Results (523)

Monitoring the stability of hydraulic structures such as dams and reservoirs in seismically active regions is essential for ensuring their safety and operational reliability. This study presents a comprehensive geospatial approach combining lineament analysis and geodynamic zoning to assess the structural stability of the Voroshilov and Priyut reservoirs located in the Almaty region, Kazakhstan. A regional lineament map was generated using ASTER GDEM data, while ALOS PALSAR data were used for detailed local analysis. Lineaments were extracted and analyzed through automated processing in PCI Geomatica. Lineament density maps and azimuthal rose diagrams were constructed to identify zones of tectonic weakness and assess regional structural patterns. Integration of lineament density, GPS velocity fields, InSAR deformation data, and probabilistic seismic hazard maps enabled the development of a detailed geodynamic zoning model. Results show that the studied sites are located within zones of low local geodynamic activity, with lineament densities of 0.8–1.2 km/km², significantly lower than regional averages of 3–4 km/km². GPS velocities in the area do not exceed 4 mm/year, and InSAR analysis indicates minimal surface deformation (<5 mm/year). Despite this apparent local stability, the 2024 Voroshilov Dam failure highlights the cumulative effect of regional seismic stresses (PGA up to 0.9 g) and localized filtration along fracture zones as critical risk factors. The proposed geodynamic zoning correctly identified the site as structurally stable under normal conditions but indicates that even low-activity zones are vulnerable under cumulative seismic loading. This demonstrates that an integrated approach combining remote sensing, geodetic, and seismic data can provide quantitative assessments for dam safety, predict potential high-risk zones, and support preventive monitoring in tectonically active regions. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article

In the context of mineral resource exploration, spatial prediction must cope with heterogeneous, non-normal data distributions and limited sampling. While conventional geostatistics and standard machine learning provide baseline estimates, they often suffer from excessive smoothing or fail to capture continuous spatial dependencies. This study proposes a geospatially aware Wasserstein conditional Generative Adversarial Network (GSA-cGAN) to complement existing workflows for multivariate mineral interpolation. The framework augments a baseline cGAN with WGAN-GP for stable adversarial training, CoordConv to encode absolute spatial coordinates and Self-Attention to capture long-range spatial dependencies. Eight model configurations were trained on 272 samples from a mineralized zone in the Taebaek Mountains, Korea, and strictly benchmarked against Ordinary/Universal Kriging and multivariate machine learning baselines (Random Forest, XGBoost). Under the adopted experimental design, the full GSA-cGAN achieved the lowest test root mean squared error and highest coefficient of determination, demonstrating a significant performance improvement over the baselines. Furthermore, distribution analysis confirmed that the model effectively overcomes the smoothing limitations of regression-based methods, generating high-resolution 10 m × 10 m maps that preserve statistical variance, hotspot anomalies, and complex spatial patterns. The results indicate that deep generative models can serve as practical decision-support tools for identifying drilling targets and prioritizing follow-up exploration in geologically complex settings. Full article

Understanding the formation logic and spatial organization of vernacular settlements requires analytical approaches that capture both morphological structures and the cognitive rules underlying residents’ interactions with space. However, existing research on rural spatial patterns has paid limited attention to the perceptual and cognitive mechanisms through which spatial genes are recognized, maintained, and reproduced. This gap limits the development of generalizable and bottom-up methods for interpreting and transmitting rural spatial characteristics. To address this gap, this study proposes a cognition-driven analytical framework supported by spatial analysis for rural space gene extraction and transmission. The framework consists of five interrelated components: environmental cognition, spatial element identification, system coupling, space gene extraction, and transmission mechanisms. The Guanzhong Region in Northwest China is selected as a representative case to examine the multi-scale spatial structure of vernacular settlements. The results reveal three major findings. (1) The proposed framework effectively links physical spatial features with local perceptual structures, enabling the identification of key elements constituting rural space gene. (2) Three categories of representative space gene and seven core morphological and functional factors are extracted through the coupled analysis of nature–settlement systems. (3) Three adaptive transmission mechanisms—element replication and reinforcement, recombination of disrupted elements, and controlled adjustment of characteristic elements—are identified to support spatial renewal while maintaining local distinctiveness. This research contributes a structured, scalable, and replicable workflow for rural space gene analysis and enhances the application of cognitive principles in geospatial modeling. The findings provide methodological and practical support for rural revitalization, cultural landscape conservation, and vernacular settlement planning in inland agrarian regions undergoing rapid transformation. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

The availability of nighttime satellite imagery provides unique opportunities for monitoring fishing activity in data-sparse ocean regions. This study leverages Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band monthly composite imagery to identify and classify recurring spatial patterns of fishing activity in the Korean Exclusive Economic Zone from 2014 to 2024. While prior research has primarily produced static hotspot maps, our approach advances geospatial fishing activity identification by employing machine learning techniques to group similar spatiotemporal configurations, thereby capturing recurring fishing patterns and their temporal variability. A convolutional autoencoder and a Gaussian Mixture Model (GMM) were used to cluster the VIIRS imagery. Results revealed seven major nighttime light hotspots. Results also identified four cluster patterns: Cluster 0 dominated in December, January, and February, Cluster 1 in March, April, and May, Cluster 2 in July, August, and September, and Cluster 3 in October and November. Interannual variability was also identified. In particular, Clusters 0 and 3 expanded into later months in recent years (2022–2024), whereas Cluster 1 contracted. These findings align with environmental changes in the region, including ocean temperature rise and declining primary productivity. By integrating autoencoders with probabilistic clustering, this research demonstrates a framework for uncovering recurrent fishing activity patterns and highlights the utility of satellite imagery with GeoAI in advancing marine fisheries monitoring. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

This study investigates the spatial patterns and driving mechanisms of China’s industrial heritage using nationwide provincial-level geospatial data. It combines multiple spatial analysis techniques to identify distribution characteristics and applies a multi-model framework integrating Multi-Scale Geographically Weighted Regression and machine learning to assess the impacts of demographic, economic, climatic, and topographic factors. Results reveal a pronounced clustered pattern and marked spatial differentiation, with core concentrations in the southeastern coastal and central regions. Industrial layouts across historical periods show a shift from coastal to inland areas, reflecting security-oriented spatial strategies. Economic development has a significant positive influence, whereas temperature and the number of industrial enterprises exert negative effects. Natural environmental conditions—such as slope, vegetation coverage, and water systems—serve as both spatial supports and constraints. At the macro level, the spatial configuration of industrial heritage emerges from the structured interplay of historical path dependence, national strategic regulation, and geographic environmental constraints, rather than short-term interactions among isolated variables. The study elucidates the evolutionary logic of industrial civilization and highlights the synergistic mechanisms linking economic, social, and environmental dimensions. It concludes by advocating a hierarchical and multi-factor balanced framework for spatial governance. Full article

Significant changes in the global climate are a focus of widespread concern, with profound implications for economies, daily life, and sustainable development. Analyzing and forecasting these trends relies heavily on meteorological data, which typically possess high-dimensional spatiotemporal attributes. Effectively extracting underlying patterns and meaningful information from such complex data is crucial for informed decision-making. This study addresses the challenge of visually representing temporal sequences within geospatial contexts, a process often hindered by the separate visualization of spatial and temporal dimensions. We propose a method that embeds a geographic map within a parallel coordinate plot: time is represented on the parallel axes, and high-dimensional attributes are encoded using color channels. This integrated view, combined with a suite of interactive techniques, enables detailed, multi-perspective, and holistic visual exploration and enhances the understanding of high-dimensional spatiotemporal meteorological data. Full article

Rapid urbanization in developing megacities has exacerbated hydrological imbalances, positioning urban flooding as a major environmental and socio-economic challenge of the twenty-first century. This study investigates the spatial relationship between building density, topography, and flood susceptibility in Jakarta, Indonesia—one of the most flood-prone urban regions globally. Employing geospatial analysis and spatial autocorrelation techniques, the research assesses how variations in land-use concentration and elevation influence the spatial clustering of flood vulnerability. The analytical framework integrates multiple spatial datasets, including Digital Elevation Models (DEMs), building footprint densities, and flood hazard maps, within a Geographic Information System (GIS) environment. Spatial statistical measures, specifically Moran’s I and Local Indicators of Spatial Association (LISA), are utilized to quantify and visualize patterns of flood susceptibility. The findings reveal that zones characterized by high building density and low elevation form statistically significant clusters of heightened flood risk, particularly within the southern and eastern subdistricts of Jakarta. The study concludes that incorporating spatially explicit and statistically rigorous methodologies enhances the accuracy of flood-risk assessments and supports evidence-based strategies for sustainable urban development and resilience planning. Full article

Burgeoning air pollution is a pressing public health concern. However, due to the scarcity and sparsity of ground-based monitoring, its impact remains uncertain. This work demonstrates how satellite-derived NO₂ observations can identify persistent pollution hotspots and seasonal patterns in a data-scarce urban region. This work leveraged TROPOMI satellite data and Google Earth Engine to evaluate tropospheric NO₂ hotspot patterns in the Greater Accra Region of Ghana from 2019 to 2023. TROPOMI data revealed persistent NO₂ hotspots in urban and industrial areas, with overall peak concentrations reaching up to 3.3 × 10¹⁵ mol cm⁻². Seasonal analysis showed elevated NO₂ levels during the dry season, with a mean concentration of 2.3 × 10¹⁵ mol cm⁻², while lower levels were observed during the rainy season. Increased emissions and reduced dispersion influence this pattern due to stable atmospheric conditions. Google Earth imagery confirmed that the highest NO₂ concentrations were associated with the Heavy Industrial Area, highlighting the presence of extensive industrial facilities such as refineries, factories, and quarries. This integration of satellite observations with high-resolution geospatial tools provides a robust methodology for NO₂ source attribution, emphasizing the need for targeted emission control measures in industrial zones to mitigate air pollution and associated health risks. Full article

With the acceleration of urbanization, the impact of built community environments on residents’ health has emerged as a research focus in urban geography and public health. This study examines 25 representative communities in Wuhan, China, employing a combination of questionnaire surveys and multi-source geospatial data. It systematically analyzes the influence patterns of built environment characteristics on residents’ self-rated health from dual perspectives: subjective perception and objective measurement. The XGBoost model was employed to achieve nonlinear fitting and prediction of residents’ self-rated health, while the SHAP method was introduced to interpret model outputs, identifying key environmental factors and their complex effect patterns. The results show that the built environment and health exhibit significant nonlinear relationships, with XGBoost outperforming other models. Residents’ health perception is jointly influenced by subjective and objective factors, with satisfaction with commercial services contributing most. Key environmental elements display threshold effects, indicating that excessive mixing may not further improve health. Furthermore, complex local interactions exist, where good transport accessibility enhances the health benefits of medical facilities and green spaces. This study demonstrates the applicability of interpretable machine learning in health geography, thus providing scientific guidance for health-oriented community planning. Full article

Gender-based violence (GBV) in urban contexts is a complex, multifactorial phenomenon shaped by socioeconomic, territorial, and contextual factors. This study aims to develop a predictive model for GBV-related crimes in Valencia (Spain), using open geospatial data and advanced machine learning techniques to support the identification of high-risk areas and guide targeted interventions. A 25 m grid was generated to homogenize crime data and independent variables, including socioeconomic indicators, urban services, real estate information, and traffic intensity. Multiple models were tested—Multiple Linear Regression (MLR), Decision Tree (DT), and Random Forest (RF). Linear models were found to be insufficient for explaining GBV patterns (R² ≈ 0.45), while RF and DT achieved high predictive accuracy (R² ≈ 0.97 and 0.95, respectively. The variables with the greatest influence were traffic intensity, average monthly income, unemployment rate, and proximity to nightlife venues. To enhance the interpretability of the most accurate models, we applied SHAP (SHapley Additive exPlanations) to quantify the contribution of each predictor and elucidate the direction and magnitude of their effects on model predictions. These findings demonstrate the utility of geospatial ML techniques in understanding the spatial dynamics of GBV and in supporting urban safety policies. While the current model focuses on static spatial predictors and does not explicitly model temporal dynamics or spatial autocorrelation, future research will integrate these aspects, along with participatory data, and test the model’s applicability in other cities to enhance its robustness and generalizability. Full article

Environmental assessment in high-density urban areas faces significant challenges due to complex building morphology and the Modifiable Areal Unit Problem (MAUP). This study proposes a morphology-adaptive computational framework that integrates the Homogeneous Unit of Building Morphology (HUBM) with geospatial modeling to enhance environmental assessment processes. Using Macao as a case study, the framework quantifies local and accessibility-based ecosystem service flows and evaluates ecological resilience via ecological security patterns and spatial elasticity indices. The results demonstrate that HUBM substantially reduces MAUP-induced biases compared to traditional grid-based approaches, maintaining statistical significance in spatial clustering analyses across all scales. Functionally, ecosystem service value (ESV) analysis reveals that natural green spaces provide more than three times the total ESV, predominantly offering regulating services, while artificial green spaces primarily deliver localized services. Accessibility analysis highlights considerable spatial inequities, with natural green spaces exhibiting a significantly higher recreational accessibility index. In terms of ecological security patterns (ESPs), natural green spaces function as core ecological patches, while artificial green spaces dominate connectivity, accounting for 75% of corridor length and 86% of node density. Natural green spaces exhibit significantly greater ecological resilience. These findings highlight the complementary roles of natural and artificial green spaces in dense urban environments and underscore the need for adaptive spatial analysis in urban planning. Full article

This study evaluates urban growth and access to drinking water in Bukavu from 1980 to 2024, combining diachronic Landsat image analysis, demographic and geospatial data, and household surveys. Bukavu’s population rose from 280,000 to over 2 million, with an annual growth rate of 4.57%, doubling every 16 years. The urbanized area expanded from 17 km² in 1984 to nearly 50 km² in 2024, with progressive densification in risk-prone zones such as steep slopes and wetlands. Theoretical access to drinking water is 61%, falling below 20% in informal neighborhoods. REGIDESO produces 25,000–30,000 m³/day, while the estimated demand is 70,000–72,000 m³/day, creating a deficit of over 30,000 m³/day. Households rely on public standpipes (45%), unimproved sources (33%), and the parallel market (44%), with average collection times of 45 min. High-density areas show elevated health risks, with 57% of water samples contaminated by Salmonella and 36% contaminated by E. coli. Land tenure insecurity affects 29.7% of households. Statistical analysis indicates strong correlations between distance and collection time (r = 0.963) and moderate correlations with disease occurrence (distance r = 0.582; time r = 0.411). These findings demonstrate that rapid urban sprawl, informal settlement, and weak institutional capacity significantly constrain water access, contributing to health risks and highlighting broader implications for African cities experiencing similar growth patterns. Full article