Search Results (1,178)

Dietary transition is reshaping cropland demand and intensifying the challenge of matching food demand with land supply in rapidly urbanizing regions. This study examines how different dietary structure scenarios generate differentiated cropland demand, how these demands match with land supply under alternative development pathways, and how the land system responds when diet-driven demand is incorporated into land-use simulation. Using Jiangsu Province, China, as a case study, we developed a coupled diet–land simulation framework. On the demand side, five dietary structure scenarios—current, balanced, U.S., Japanese, and Greek—were constructed based on seven food categories, and their cropland demand in 2035 and 2050 was estimated using the cropland footprint approach and LSTM forecasting. On the supply side, the GeoSOS-FLUS model was used to simulate future land-use patterns under four development scenarios: natural development, cultivated land protection, ecological protection, and economic development. The cropland demand associated with each dietary scenario was then introduced into the land-use simulation process as an external demand constraint to identify land-system feedbacks and scenario differences. The results show that cropland demand differs markedly across dietary scenarios, forming a clear gradient from moderate-demand to high-demand diets. These differences are driven primarily by changes in the composition of key food categories, especially grains, livestock and poultry meat, plant oils, and fruits, rather than by proportional increases across all foods. In terms of supply–demand matching, the cultivated land protection scenario provides the strongest support for high-demand diets, whereas the natural development, ecological protection, and economic development scenarios are more compatible with moderate-demand dietary pathways. Once diet-driven demand is incorporated into land-use simulation, the land system shows clear sensitivity and strong scenario dependence. High-demand dietary scenarios intensify cropland compensation pressure and trigger structural reallocation among cultivated land and flexible land types. Under natural development, the response is mainly reflected in cropland expansion and grassland compression; under cultivated land protection and ecological protection, it is expressed more through substitutions among grassland, water bodies, and unused land; under economic development, the most prominent feedback is the competitive reallocation among cultivated land, construction land, and water bodies, with high dietary demand even constraining construction land expansion. Overall, the robustness of cropland supply–demand matching depends not only on the scale of dietary demand but also on how different dietary pathways interact with development-oriented land-use structures. Full article

Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping priorities, classification instability near national boundaries undermines transboundary comparisons. To address this, we propose a Conflict-aware Adaptive Distillation Fusion Network (CADF-Net) that fuses multi-source land-cover products to improve the discrimination and spatial consistency of key vegetation classes in cross-border regions. Taking the transnational China–Russia border (Sanjiang Plain and Primorskiy Kray) as a representative case, we integrate geo-environmental factors and introduce a pixel-level Conflict Index (CI) to explicitly steer the model toward discrepancy-prone areas. Building on this, we develop an Adaptive Distillation U-Net (AD-UNet) with uncertainty-adaptive distillation and employ a confidence-guided, dynamically weighted ensemble to generate the final fused land-cover product (CADF-LC). Quantitative assessments demonstrate that CADF-LC achieved an OA of 0.8600, a Kappa of 0.8133, and an mIoU of 0.7589, outperforming all input land-cover products. Compared with the strongest input product, Esri Land Cover, CADF-LC improved OA by 0.0150 and mIoU by 0.0222. Furthermore, it effectively mitigates the trade-off between detail loss and morphological fragmentation. Ultimately, CADF-Net enhances classification stability for key vegetation classes, offering a reliable foundation for transboundary ecological monitoring and land management. Full article

Efficient soil fertility monitoring is essential for sustainable agriculture, food security, and environmental management across Africa, yet conventional laboratory methods remain prohibitively costly and slow for continental-scale applications. Soil spectroscopy is considered as a rapid, non-destructive alternative with transformative potential. This review provides a systematic synthesis of spectroscopic applications across Africa, encompassing laboratory, field, airborne, and satellite-based platforms, while examining major data sources including the Africa Soil Information Service (AfSIS) and GEO-CRADLE spectral libraries. We critically evaluate the evolution of modeling approaches, revealing that Partial Least Squares Regression (PLSR) dominates, but a shift toward advanced frameworks like hybrid physically based models, ensemble learning and deep neural networks is essential. Critically, we identify a pronounced imbalance wherein laboratory spectroscopy prevails while imaging and satellite-based approaches remain comparatively underutilized, despite their unparalleled potential for scaling point measurements to continental extents. The review consolidates findings on key soil properties, demonstrating consistent successes for primary constituents with direct spectral responses (i.e., organic carbon), while revealing relative uncertainty for properties inferred indirectly via covariance (e.g., available phosphorus, potassium). Despite significant local and regional progress, the absence of a standardized pan-African spectral library and the intractable transferability problem remain formidable barriers. Future research must pivot decisively toward imaging spectroscopy and satellite platforms, mitigating PLSR dominance through systematic adoption of ensemble methods, transfer learning, and model harmonization frameworks to fully operationalize these technologies in support of Africa’s sustainable development goals. Full article

Scientifically assessing the spatiotemporal evolution of regional carbon storage is of great significance for achieving the “dual carbon” goals and optimizing territorial spatial patterns. This study integrated the PLUS and InVEST models to systematically reconstruct the spatiotemporal pattern of carbon storage in Hebei Province from 2000 to 2020, simulate its evolution trajectory under different scenarios in 2030, and identify its driving mechanisms using the GeoDetector model. The main findings are as follows: (1) From 2000 to 2020, cropland was the dominant land use type in Hebei Province, and carbon storage exhibited a spatial pattern of “high in the northwest, low in the southeast.” Carbon storage increased from 16.23 × 10⁸ t to 16.31 × 10⁸ t, with a significantly slowed growth rate after 2010. (2) Multi-scenario simulations for 2030 indicate that under the natural development and economic priority scenarios, construction land expands significantly while cropland and grassland continue to decrease. In contrast, carbon storage shows an increasing trend under the ecological protection and cropland protection scenarios. (3) Driving factor analysis reveals that the spatial differentiation of carbon storage is primarily controlled by natural factors such as slope, elevation, and NDVI, while the explanatory power of anthropogenic factors, particularly population density, has significantly increased. The interaction between NDVI and slope exhibits a synergistic enhancement effect. This study elucidates the coupling mechanisms between land use change and carbon storage under different policy orientations, providing a scientific basis for territorial spatial optimization and the formulation of differentiated carbon neutrality pathways in Hebei Province. Full article

Traditional villages embody regional culture and local knowledge, yet culture–tourism integration often suffers from a mismatch between resource value and effective transformation. To address this problem, this study proposes a two-dimensional “benefit–obstacle” diagnostic and strategy-matching framework and tests its case-based applicability in Dayuwan Village. First, a cultural landscape gene (CLG) atlas was constructed for the village based on a geo-information coding scheme, covering both tangible and intangible CLGs. Second, a four-dimensional evaluation system was operationalized through five expert judgments and 106 valid on-site questionnaires collected from tourists (n = 67) and residents (n = 39). Criterion weights were determined using an AHP–entropy combination approach, and the comprehensive benefit closeness coefficient was calculated via TOPSIS. Third, an obstacle degree identification model was employed to pinpoint key constraints and derive composite obstacle degrees. Results within the Dayuwan case show that the TOPSIS closeness coefficients of the 17 genes ranged from 0.653 to 0.782 (mean = 0.714), with 4, 6, and 7 genes classified as excellent, good, and medium, respectively; composite obstacle degrees ranged from 0.0228 to 0.1975. In Dayuwan Village, higher obstacle degrees clustered mainly in intangible CLGs, whereas Ming–Qing architecture and frequently practiced folk-cultural genes showed comparatively lower obstacle degrees. The transformation process is constrained by four mechanisms—landscape character protection, economic transformation, social identity, and market demand—with economic transformation constraints being the most prominent. Based on the benefit–obstacle matrix, 17 CLGs were classified into five activation scenarios and matched with corresponding revitalization strategies. This framework links benefit ranking, obstacle diagnosis, and strategy matching, and provides a case-based diagnostic reference for the conservation and culture–tourism integration of villages with comparable heritage conditions, subject to local recalibration of indicators, weights, and thresholds. Full article

Sub-sag 21 in the eastern Yangjiang Sag, Pearl River Mouth Basin, South China, contains a thick lacustrine source-rock interval within the lower Wenchang Formation and is a major exploration target on the northern margin of the South China Sea. However, the timing of deposition during the early to middle Eocene remains poorly constrained, and the applicability of quantitative palaeoclimate reconstruction methods in low-latitude lacustrine basins requires further evaluation. In this study, we analyzed mudstones from the lower Wenchang Formation in Well E1. Using cyclostratigraphic constraints, we applied AstroGeoFit to construct an astronomically tuned age model, and combined palynological coexistence analysis with geochemical weathering proxies and linear–regression calibration to quantitatively reconstruct and cross-validate mean annual temperature and mean annual precipitation. Within this time-calibrated framework, we further quantified organic-carbon burial to evaluate the relationship between palaeoclimate evolution and organic-matter enrichment. The AstroGeoFit results indicate that the top of the lower Wenchang Formation in Well E1 is constrained to 44.563 Ma, and that the studied succession spans 50.249–44.563 Ma. Palynological coexistence analysis identifies three palaeoclimate phases within this interval. Method evaluation shows that the temperature reconstruction based on major-element geochemistry agrees well with the pollen-based temperature record, whereas one precipitation reconstruction based on weathering proxies shows the most robust agreement and stability relative to the pollen-based precipitation record. Reconstructed mean annual temperature ranges from 10.77 to 22.20 °C, and reconstructed mean annual precipitation ranges from 1188.27 to 1871.89 mm. Correlation analyses on the tuned timescale show that precipitation is more strongly associated than temperature with organic-matter accumulation parameters, including total organic carbon and organic carbon accumulation rate, indicating that organic carbon burial in the eastern Yangjiang Sag lake basin was mainly controlled by hydrological forcing. During the Early Eocene Climatic Optimum, carbon burial in low-latitude lakes was, therefore, not a simple response to elevated temperature, but instead reflected the integrated effects of precipitation, runoff, stratification, material supply, transport, and preservation. The evolutionary sequence further suggests that early high productivity was diluted by rapid sedimentation, reducing total organic carbon; subsequent cooling, lake deepening, and strengthened stratification enhanced organic matter preservation; and finally, tectonic subsidence together with regional humidification promoted the development and long-term preservation of high-quality lacustrine source rocks. Full article

Accurate, scalable estimation of rice planting dates is essential for climate-adaptive management in multi-cropping regions, yet most models rely on static calendars, which fail to capture climate-driven shifts and bias simulated yield responses. This study aims to develop a climate-driven, spatially explicit framework to simulate dynamic transplanting dates across diverse multi-cropping systems in monsoon Asia. Utilizing daily AgERA5 reanalysis and Monsoon Asia Rice Calendar (MARC) data from 2019 to 2020, we present Geo-ROCKET. The framework integrates an automated K-means clustering workflow to delineate bimodal planting windows and employs random convolutional kernel transforms with adaptive geographic neighborhoods to capture local climate heterogeneity. Evaluated by area-weighted mean absolute error (MAE), the model achieves high accuracy across six seasons (MAE 6.53–12.50 days), outperforming six traditional ROCKET and ensemble baselines while preserving smooth spatial error fields. Sensitivity experiments reveal that a 15-day bias in the previous harvest date can increase transplanting error to 10.8–17.8 days, emphasizing the importance of sequential consistency. By providing dynamic, climate-sensitive inputs, Geo-ROCKET improves the accuracy of crop modeling for climate impact projections. This framework offers a flexible tool for characterizing human management decisions and evaluating adaptation strategies in intensive agricultural systems. Full article

Regular social, economic and agricultural interactions occurred between the Horn of Africa and Southern Arabian Peninsula for millennia, raising questions about potential geo-culinary exchanges, including of the little-studied fermented flatbreads produced in these areas. A comparative analysis of Somali laxoox/canjeero, Ethiopian injera, Sudanese kisra and Yemeni/Saudi lahoh was conducted by combining a literature review and consultations with 17 local experts, then processing the data in a hierarchical cluster analysis to quantify “biocultural” diversity. In an interdisciplinary approach, technical aspects (bread appearance, ingredients, and production stages) and cultural characteristics (consumption patterns and social function) were considered to identify key descriptors of the breads. A dendrogram generated through cluster analysis of a binary (0/1) matrix, structured with the key descriptors, showed that each bread has a distinct biocultural identity, and enabled the quantification of their similarities. Somali laxoox/canjeero and Yemeni/Saudi lahoh had a 64% similarity to each other (Jaccard index); each had a 53% similarity to Ethiopian injera; while all of them were 41% similar to Sudanese kisra. Hierarchical cluster analysis, applied for the first time to flatbreads, contributes to their comprehensive characterization and comparison in this unique geographic region and lays the foundations for policies to protect their identity and quality. Full article

Worldwide visual geo-localization aims to predict the geographic coordinates of an image capture location from visual content alone, posing unique challenges due to the vast scale of the Earth’s surface and pervasive visual ambiguity across distant regions. Existing approaches face distinct limitations as follows: retrieval-based methods demand massive geo-tagged databases and scale poorly; alignment-based models lack interpretability and are vulnerable to visually similar scenes; and large vision-language models (LVLMs) offer semantic reasoning but suffer from hallucination. A natural solution is retrieval-augmented generation (RAG), yet we observe that directly injecting retrieved candidates as context causes severe context poisoning. To address this, we propose HybridGeo, a dual-stream late-fusion framework that decouples retrieval from reasoning. A retrieval stream applies continuous alignment with spatial–semantic clustering to produce stable regional anchors; a reasoning stream performs context-free Chain-of-Thought inference to yield an independent coordinate estimate. The two streams are fused only at the decision stage via a spatial–consistency module that triggers weighted averaging under agreement or confidence-based arbitration under conflict. Experiments on Im2GPS3k show that HybridGeo achieves 73.89% Country@750km accuracy, outperforming the retrieval baseline by 7.27% and 8.23%, and surpassing both VLM-only and RAG baselines. These results demonstrate that late fusion effectively avoids context poisoning while enabling complementary benefits from both streams. Full article

Cross-view geo-localization (CVGL) aims to match ground-level images with geo-tagged aerial imagery for precise localization, but remains challenging due to severe viewpoint discrepancies, partial correspondence, and significant domain shifts across geographic regions. While existing methods achieve high accuracy within specific datasets, their generalization ability to unseen environments is limited. In this paper, we propose GenGeo, a unified framework that integrates vision foundation model representations with a matching-aware aggregation mechanism to address these challenges. Specifically, we leverage DINOv2 to extract semantically rich and transferable features, and revisit the SALAD aggregation module in the context of CVGL. By employing a shared clustering strategy, the proposed framework projects cross-view features into a unified assignment space, enabling implicit semantic alignment across views, while the dustbin mechanism effectively filters unmatched and non-informative regions arising from partial correspondence. Extensive experiments on three large-scale benchmarks (CVUSA, CVACT, and VIGOR) demonstrate that GenGeo achieves state-of-the-art performance in cross-dataset generalization and consistently improves robustness under severe domain shifts and spatial misalignment. Notably, our method outperforms the baseline by 14.65% in Top-1 Recall on the CVUSA-to-CVACT transfer task. These results highlight the effectiveness of combining foundation model representations with matching-aware aggregation, and suggest that enforcing semantic consistency in a shared assignment space is a promising direction for generalizable cross-view geo-localization. Full article

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. Full article

Railway transport plays a crucial role in sustainable and low-carbon mobility; however, the safety and resilience of railway systems are increasingly challenged by aging infrastructure, spatial inequality, and intensifying climate extremes. These challenges are particularly evident in Serbia, where railway stations in rural and peripheral areas often lack adequate safety infrastructure, accessibility, and climate-adaptive design, especially affecting women and other vulnerable passengers. The aim of this study is to develop a GIS-based spatial framework for assessing gender-sensitive railway safety under combined sociospatial and environmental pressures. The analysis integrates multiple geo-information sources, including railway infrastructure data, passenger statistics, safety incidents, and climate hazard indicators such as floods, heatwaves, heavy snowfall, and windstorms. Geographic Information System (GIS) techniques, including kernel density estimation, buffer and zonal statistics, spatial interpolation, and spatial regression, were applied to evaluate spatial safety patterns and environmental risks. The results reveal pronounced regional disparities, with southern and eastern Serbia representing the most vulnerable areas due to inactive stations, poor lighting, limited digital connectivity, and frequent exposure to extreme weather events. Rural railway stations are frequently located in climate risk zones, and many do not meet the minimum safety infrastructure standards. Based on these findings, this study recommends strengthening station lighting and surveillance systems, improving digital connectivity and emergency accessibility, and integrating climate-resilient infrastructure planning into railway modernization strategies. Overall, the findings highlight the importance of combining GIS-based spatial analysis, climate hazard assessment, and gender-sensitive planning to support safer, more inclusive, and climate-resilient railway infrastructure in Serbia. Full article

This study used Global Navigation Satellite System (GNSS) observations from the China Crustal Movement Observation Network (CMONOC) and the Kunming Continuously Operating Reference Station (KMCORS) network to investigate ionospheric response characteristics over China during the geomagnetic storm of 4–6 November 2023, and to assess their impacts on CORS-based real-time kinematic (RTK) positioning performance in the low-latitude Kunming region. A quantitative assessment was conducted by integrating regional two-dimensional dTEC (%) maps over China, BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) total electron content (TEC), the rate of TEC index (ROTI), and RTK positioning solutions to evaluate ionospheric disturbances, irregularity activity, and associated degradation in positioning performance. Results indicate that, during geomagnetic storms, ionospheric responses over China exhibit pronounced phase-dependent and latitudinal variations. During the second geomagnetic storm on 5–6 November, positive responses were dominant at mid-to-high latitudes, whereas alternating positive and negative responses were observed at low latitudes. During the recovery phase, the Kunming region successively experienced a positive ionospheric storm lasting approximately 10 h, followed by a negative ionospheric storm lasting about 7 h, with relative TEC variations reaching a maximum of approximately 90%. The GEO TEC time series was consistent with the temporal evolution of the two-dimensional dTEC (%), while ROTI increased markedly during the disturbance enhancement period (21:00 UT on 5 November to 07:00 UT on 6 November 2023). During periods of enhanced ionospheric response and irregularities, RTK positioning performance was observed to deteriorate markedly. The fixed-solution rate at medium-to-long baseline stations decreased from nearly 100% to close to 0%, accompanied by an increase in vertical positioning errors to approximately 20 cm, whereas short-baseline stations were only minimally affected. These results indicate that ionospheric disturbances during geomagnetic storms exert a pronounced impact on CORS-based RTK positioning services in the Kunming region, with the magnitude of this impact being closely related to baseline length. Full article

Autonomous mobile robots operating in outdoor environments must have an understanding of the surrounding terrain geometry to ensure efficient and safe navigation. This article presents a DEM-based intelligent traversability mapping framework to transform open-source geospatial data into slope-aware cost maps for multirobot autonomous navigation within the ROS2 framework. The proposed cv_gdal algorithm automatically processes GeoTIFF elevation data using adaptive slope thresholding based on each robot’s physical capabilities, generating ROS-compatible cell occupancy maps. Six regions of Spain were used to evaluate terrain representation accuracy and navigation performance in kilometer-scale DEMS. This framework enables autonomous perception-to-planning pipelines and supports the deployment of multirobot systems for search and rescue (SAR) tasks. By bridging geospatial analytics with robotic perception and adaptive decision-making, this work contributes to the development of intelligent, self-configuring robotic systems capable of operating safely in complex outdoor environments. Full article

Satellite observations of methane are frequently compromised by extensive data gaps caused by cloud cover and aerosol contamination, limiting their utility for continuous regional monitoring. To reconstruct these spatiotemporal discontinuities, this study developed the Stacked Ensemble Reconstruction Framework for Methane (SERF-XCH₄). By integrating Sentinel-5P TROPOMI retrievals with 25 multi-source environmental covariates, we generated a spatiotemporally continuous, high-resolution (0.1°) monthly dataset (SERF-XCH₄-IM) for Inner Mongolia spanning 2019 to 2023. Comprehensive validation demonstrates that the framework achieves exceptional predictive fidelity with a Coefficient of Determination ($R^2$) of 0.93 and a Root Mean Square Error (RMSE) of 7.89 ppb, significantly surpassing the performance of individual base learners and traditional interpolation methods. Furthermore, spatial block cross-validation confirmed robust generalization capabilities ($R^2=0.90$) in data-void regions. To unravel the “black box” of the model, SHapley Additive exPlanations (SHAP) analysis was employed, revealing that temporal factors (contributing 63.9%), air temperature, and elevation are the dominant drivers governing XCH₄ variability. Spatiotemporal analysis further identified the Hulunbuir region as a significant growth “hotspot” with an annual increase rate exceeding 18.5 ppb/yr, a trend primarily driven by intensified emissions during the autumn and winter seasons. Consequently, this framework establishes a high-precision, interpretable paradigm for regional methane monitoring and geo-information reconstruction. Full article