Search Results (1,378)

Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations. This study proposes a “Frugal AI” distributed multi-GPU framework that optimizes hyperparameters via a stochastic simplex-based search coupled with five-fold cross-validation. Utilizing three low-cost NVIDIA GTX 1050 Ti GPUs, the framework performs parallel candidate exploration with an asynchronous model-level exchange mechanism to escape local optima without the overhead of gradient synchronization. Seven CNN backbones—VGG16, VGG19, GoogLeNet, MobileNetV2, ResNet18, ResNet50, and ResNet101—were evaluated within YOLOv2 and Faster R-CNN detectors. To address memory constraints (4 GB VRAM), YOLOv2 was selected for extensive benchmarking. Performance was measured using a harmonic precision–recall-based cost metric to strictly penalize imbalanced outcomes. Experimental results demonstrate that under identical wall-clock time budgets, the proposed framework achieves an average 1.38% reduction in aggregated cost across all models, with the highly sensitive VGG19 backbone showing a 4.00% improvement. Benchmarking against Bayesian optimization, genetic algorithms, and random search confirms that our method achieves superior optimization quality with statistical significance ($p < 0.05$). Under a rigorous IoU = 0.75 threshold, the optimized models consistently yielded F1-scores 0.8444 $\pm$ 0.0346. Ablation studies further validate that the collaborative model exchange is essential for accelerating convergence in rugged loss landscapes. This research offers a practical, scalable, and cost-efficient solution for deploying robust AI surveillance in resource-constrained smart city infrastructure. Full article

Land cover (LC) change is reshaping terrestrial ecosystems and profoundly impacting sustainable development in Africa, yet the long-term, continental-scale spatiotemporal dynamics of these shifts remain obscured. To address the above issue, this study systematically explores the spatiotemporal dynamics of LC across Africa from 1985 to 2022 by leveraging the fine-resolution remote-sensing-derived GLC_FCS30D LC dataset within a stratified Intensity Analysis framework. To decompose landscape changes into interval, category, and transition levels across five climatic sub-regions of Africa, we systematically evaluate the temporal consistency of land systems. This hierarchical approach disentangles systematic transition pathways from random fluctuations, thereby revealing the distinct regional regimes governing continental transformation of LC. Our results ultimately show a strong LC change acceleration in Africa after 2010, mainly in Southern, Eastern, and Western Africa, which together made up 80 to 90% of the continent’s LC dynamics. During the whole study period, shrubland and grassland had the highest gross turnover due to their high bidirectional volatility. Intensity-wise, forest remained inactive even though it was a persistent net loser to crop in East Africa (2010–2020), to shrub in Southern Africa (1990–2022), and to wetland in West Africa during the post-2000 intervals. Wetland had a major change in dynamics from historical growth during 1985–1990 to systematic decline in 2015–2022. Cropland increased by systematically targeting shrubland and grassland, mainly in East Africa. Additionally, the Sahel contributed 40% of continental grassland to bare area transitions, despite some recovery of grassland in the region. These findings show that aggregate net-change metrics obscure the volatility in African LC; therefore, distinct regional regimes such as agricultural expansion and forest degradation necessitate spatially differentiated management strategies. Full article

This study investigates a typical valley city, Lanzhou, China, to reveal the nonlinear relationships and interaction mechanisms between gray–green space morphology and seasonal diurnal land surface temperature (LST) using multi-source remote sensing and land use data. A comprehensive morphological indicator system encompassing scale, complexity, connectivity, and structural integrity was constructed through landscape metric screening and the CRITIC objective weighting method, combined with the XGBoost-SHAP explainable machine learning framework. The findings highlight that: (1) Gray–green space impacts on LST exhibit significant seasonal and diurnal variations—daytime LST is predominantly governed by gray space morphology (e.g., fragmentation degree), while nighttime LST is driven by green space morphology (e.g., coverage intensity). (2) Key indicators demonstrate pronounced nonlinear and threshold characteristics: the cooling effect of green space coverage intensity (GCI) saturates beyond 0.25; gray space morphological structure factor (GRMSF) demonstrates cooling potential when exceeding 0.25, mitigating its warming effect. (3) Significant synergistic interaction effects exist between gray and green spaces. Interaction analysis reveals that “high green coverage with low structural connectivity of gray space” produces optimal synergistic cooling effects, representing the most effective spatial configuration for nighttime LST mitigation. This study deepens theoretical and methodological understanding of the complex relationships between spatial morphology and thermal environments, providing quantified, temporally differentiated spatial optimization guidance for climate-adaptive planning in valley cities. Full article

Online reviews are widely used to evaluate tourism performance, but it remains unclear whether platform ratings adequately reflect the underlying tourist experience. This study uses 67,744 cleaned Ctrip reviews from 112 A-level scenic spots in Liaoning Province, China, to examine what online reviews reveal beyond conventional satisfaction metrics. The final analytical sample comprises 106 threshold-qualified attractions with at least 100 reviews, supplemented by six highly reviewed sub-attractions that were listed separately on the platform but belonged to officially recognized A-level scenic systems. We combine topic modelling, sentiment analysis, and a rating–sentiment analytical framework to identify experiential dimensions, emotional patterns, and attraction-level sentiment risk. The results reveal a five-dimensional structure of tourist experience, including accessibility and ticketing, natural landscape imagery, cultural heritage interpretation, service-process quality, and overall affective appraisal. Positive sentiment is concentrated in landscape, heritage, and holistic appraisal themes, whereas negative sentiment is more prominent in accessibility and service-process dimensions. Quadrant-based analysis further shows that favourable ratings may coexist with relatively negative textual sentiment, suggesting that platform ratings and review-text sentiment do not fully converge. To extend review-level evidence to the attraction level, the study develops an attraction-level sentiment-risk indicator that captures the concentration of sentiment-negative reviews within each scenic spot. The findings suggest that online reviews function as a dual-channel evaluative system and can support sustainable destination management through more sensitive monitoring of operational friction and experiential risk. Full article

Building Information Modeling (BIM) has changed the landscape of the architectural, engineering, and construction (AEC) industry in recent decades. However, BIM is not well researched in most developing countries; in particular, few studies have addressed its adoption in Malawi. A non-probability, purposive sampling approach was adopted. A total of 143 questionnaires were completed. This research reveals that, while construction experts are aware of BIM, the level of uptake remains quite low. Architects in Malawi are the most knowledgeable, followed by land surveyors and then engineers. This research shows that most experts in Malawi are at level 1 of BIM usage, which is the first stage of BIM adoption and is characterized by the use of 3D models and output representation. Furthermore, the study results have shown that the Malawian AEC sector is currently succeeding at the modelling stage of maturity but is stalled by lack of collaborative frameworks, such as Integrated Project Delivery (IPD). Therefore, unless the industry shifts toward a unified Common Data Environment (CDE), advanced capabilities like clash detection will remain underutilized and disconnected from broader project success metrics. Statistical analysis has shown that the correlation analysis demonstrates a strong link (r = 0.75) between Integrated Project Delivery (IPD) and high BIM maturity, whereas traditional Design-Bid-Build methods show a critical misalignment with digital workflows. The study identifies high software costs and a lack of national standards as the primary barriers to adoption. Therefore, there is a need for robust sensitization to the benefits of BIM and training to improve its uptake in the context of Malawi’s construction industry. In order to advance Malawi’s BIM maturity, the research recommends a strategic shift toward integrated procurement models, the establishment of national BIM mandates, and the modernization of technical education to bridge the existing knowledge gap. Full article

Satellite remote sensing has rapidly evolved from an experimental support tool into a structural component of preventive archaeology and cultural heritage governance. Drawing on scientific publications and policy-oriented grey literature from 2010–2025, this study provides an integrated review of how optical, SAR, and multi-sensor satellite data are used to detect archaeological sites, monitor landscape and structural change, and support risk-informed planning across diverse legal and institutional contexts. A multi-platform workflow combines AI-assisted semantic querying (Consensus), bibliometric searches (Scopus), and the collaborative management and geospatial visualisation of references through Zotero, VOSviewer (1.6.19), and QGIS (3.44)-based literature mapping, thereby linking thematic trends, co-authorship networks, and geographical patterns of research and regulation. The results show non-linear but marked publication growth, a strongly interdisciplinary profile, and the consolidation of international hubs that drive advances in Sentinel-2-based prospection, Landsat and night-time lights urbanisation metrics, and SAR time series for deformation, looting, and conflict-damage mapping. Parallel analysis of grey literature and institutional initiatives (Copernicus Cultural Heritage Task Force, national “extraordinary plans”, regional declarations, and UNESCO guidelines) reveals the codification of satellite Earth observation within rescue archaeology protocols, emergency archaeology, and long-term conservation strategies. Overall, the evidence indicates a transition towards data-driven, multi-sensor, and multi-scalar research, underpinned by open satellite data, reproducible workflows, and AI-supported evidence synthesis. Full article

Peri-urban areas (PUAs), as transitional zones between urban and rural regions, play a critical role in supporting food systems and agricultural livelihoods, yet they are increasingly pressured by rapid urban expansion. Reliable spatial delineation of PUAs remains challenging, as administrative boundaries often fail to capture their functional and spatial heterogeneity. This study proposes a multi-dimensional, spatially explicit framework to delineate peri-urban areas using Indonesia as a case study. Eighteen indicators representing six analytical dimensions—land use/land cover, economic, demographic, infrastructural, spatial accessibility, and landscape structure—were derived from remote sensing and GIS-based data sources and integrated into a composite scoring system using equal-weighted and AHP-weighted approaches. The framework was applied to four major cities on Java Island (Jakarta, Surabaya, Bandung, and Yogyakarta) to generate continuous peri-urban probability surfaces, which were validated using expert surveys across 25 districts in the Jakarta and Bandung metropolitan areas. The results show that the framework effectively captures the spatial heterogeneity and gradients of peri-urban areas, with the equal-weighted approach exhibiting statistically significant agreement with expert assessments (Pearson’s r = 0.517, p = 0.008; Spearman’s ρ = 0.522, p = 0.008; Kendall’s τ = 0.387, p = 0.008), consistently outperforming the AHP-weighted model across all validation metrics. The proposed approach provides a transferable spatial mapping framework for monitoring peri-urban dynamics in rapidly urbanizing regions using remote sensing and GIS. Full article

Accurate soil thermal resistivity is crucial for real-time cable ampacity determination to maximize cable utilization. However, the determination of soil thermal resistivity involves solving a computationally expensive multi-physical field inverse problem where a high-fidelity model (HFM) is used for performance evaluations. Surrogate-assisted evolutionary algorithms (SAEAs) are computationally efficient for such problems; model management strategies (MMSs) are key to SAEAs. Nevertheless, most MMSs struggle to balance the computational cost and the search accuracy due to their reliance on fitness value errors. In fact, maintaining a similar function landscape between the surrogate and the HFM is more essential than achieving precise fitness values on the surrogate. Consequently, a rank-based hybrid MMS-driven two-stage SAEA is proposed. Stage 1 focuses on identifying promising regions. To ensure the similarity between the surrogate and HFM function landscape and thus guide the evolution accurately, a global MMS is proposed. Specifically, a new function landscape similarity metric is proposed to adaptively adjust the surrogate update frequency. A new rank-error-based individual selection strategy selects key individuals for exact evaluations to refine the surrogate similarity. Stage 2 performs a refined local search within the identified promising region, utilizing a local MMS to re-evaluate the optimum of a local surrogate built around the best solution searched in Stage 1. Optimization results confirm the proposed method’s superiority on test functions and a prototype cable. Full article

Understanding how landscape structure affects nutrient pollution is essential for contemporary effective river basin management. This study examined the influence of landscape composition and configuration on concentrations of nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonium (NH₄⁺) in 30 small lowland catchments of central–eastern Poland during the cold period. Water samples were collected monthly from September 2021 to April 2022, and land-use patterns were quantified using landscape metrics derived from high-resolution spatial data at the catchment scale and within riparian buffer zones. The results showed that the impact of land use on nitrogen concentrations was strongly dependent on both landscape type and spatial scale. Forests, meadows, wetlands, and water bodies generally acted as sink landscapes, reducing nitrate and nitrite levels. The effect was more pronounced in catchments where forest patches (mainly coniferous) covered a larger area, had greater total Edge Length, and were more complex in shape. It was advantageous when meadow patches were large, cohesive, and weakly fragmented. In contrast, arable land and built-up areas consistently functioned as source landscapes, contributing to higher nitrogen concentrations when characterized by a larger share, size (both), and aggregation degree of patches (arable land). Higher landscape diversity at the catchment scale was associated with lower nitrate and nitrite concentrations. Overall, land-use effects were best explained at larger spatial extents, especially the entire catchment and the 500 m buffer zone. These findings emphasize the need to integrate landscape structure and appropriate spatial scale into nutrient management strategies for lowland agricultural catchments. Full article

Controlling carbon dioxide (CO₂) emissions remains a central challenge in Indonesia’s energy transition and its commitment to achieving net-zero emission targets. Carbon Capture and Storage (CCS) and Carbon Capture, Utilization, and Storage (CCUS) are widely recognized as important mitigation pathways, particularly for energy and industrial sectors where rapid decarbonization remains difficult. In parallel, cold plasma technology has emerged in the recent scientific literature as an early-stage, non-thermal approach for CO₂ activation under relatively low bulk temperature conditions, attracting interest as a potential long-term research pathway. This paper examines cold plasma technology within the broader CCS/CCUS landscape in Indonesia from a policy and technology perspective. The study adopts a qualitative and descriptive approach, synthesizing the selected academic literature on plasma-based CO₂ conversion, global CCUS development trends, and Indonesia’s regulatory, infrastructural, and energy system context. Rather than assessing techno-economic feasibility, the analysis focuses on identifying structural constraints, performance trade-offs, and policy-relevant considerations. The findings indicate that across plasma configurations, including dielectric barrier discharge, gliding arc, microwave, and radio frequency plasmas, current research outcomes remain constrained by low energy efficiency, limited scalability, and low technology readiness for large-scale applications. Reported performance metrics are largely derived from laboratory-scale studies under controlled conditions and cannot yet be extrapolated to real-world emission sources without a comprehensive system-level evaluation. Compared with established CCS and CCUS pathways, cold plasma technologies remain exploratory and lack the maturity required for near-term deployment. From a policy and research perspective, cold plasma should therefore be regarded as a long-term research option rather than an implementable mitigation solution for Indonesia, with its potential contribution lying in informing future research agendas, technology monitoring, and innovation planning, particularly in relation to CO₂ utilization concepts and decentralized energy systems, contingent upon significant advances in energy performance, system integration, and standardized evaluation frameworks. Full article

This study developed and tested a neighborhood-scale framework that integrates unmanned aerial vehicle (UAV)-based multispectral mapping and georeferenced socioeconomic data to identify inequities in urban green infrastructure and translate them into an operational prioritization tool for inclusive planning. Using object-based image analysis, impervious surfaces, low vegetation, and woody vegetation (trees and shrubs) were mapped across 33 Neighborhood Units in Temuco, Chile, and landscape metrics describing dominance, edge, isolation/connectivity, and diversity were derived. Socioeconomic conditions were summarized through Principal Component Analysis, and their relationships with vegetation metrics were evaluated using Generalized Additive Models. The results revealed strongly nonlinear and metric-specific associations, with the most robust relationships observed for woody-structure metrics, particularly total woody edge and built-environment isolation, whereas landscape diversity showed weaker but still significant dependence on resource-access gradients. To support inclusive planning, a dimensionless Green Infrastructure Prioritization Index (GIPI) was computed by combining standardized green deficit and standardized social vulnerability with equal weights. GIPI values ranged from 0.318 to 0.740 (median = 0.528), identifying 11 high-priority units characterized by higher social vulnerability and less favorable woody structure, including lower largest-patch dominance and greater isolation. Sensitivity analyses varying the deficit weight from 0.30 to 0.70 showed that 10 of the 11 high-priority units remained in the same class in at least 80% of weighting scenarios, indicating a stable priority set. Further classification of high-priority units according to dominant deficit type supported a staged intervention strategy, in which woody canopy is first increased in deficit nodes and subsequently reinforced through corridor-oriented greening to improve structural connectivity. These findings demonstrate the value of coupling fine-scale vegetation mapping with socioeconomic gradients to support more equitable urban green infrastructure planning. Full article

Climate change threatens the stability of temperate grassland ecosystems in Inner Mongolia, a core part of the Eurasian Steppe, by driving widespread shifts in plant species distributions. Agropyron cristatum (L.) Gaertn., a dominant native perennial herb in Inner Mongolian steppes, is ecologically vital for degraded grassland restoration and forage supply, but its response to future climate change is unclear. Here, we used an optimized MaxEnt model to assess its potential distribution under current and future climate scenarios. We processed 228 initial occurrence records into 112 valid points, selected 11 non-collinear environmental variables, optimized model parameters with the R package ENMeval, and projected distributions for the 2050s and 2100s under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios, while quantifying habitat fragmentation with landscape metrics. We found that annual mean temperature and annual precipitation dominate A. cristatum distribution (total contribution ~87%), with current highly suitable habitats concentrated in central-eastern Inner Mongolia. Future scenarios show stable core suitable habitats with northward and westward shifts, habitat fragmentation will slightly increase. Our findings clarify the climate response of A. cristatum and support its conservation and adaptive grassland management. Full article

This study investigated seasonal and sex-based differences in psychological responses and area-of-interest (AOI)-based visual attention, as well as the associations between these variables, using images of the same forest-healing landscape captured in summer and autumn. A total of 40 adults (20 males and 20 females) participated in an eye-tracking experiment combined with psychological assessments, including the Perceived Restorativeness Scale (PRS-11) and semantic differential (SD) evaluations. Mixed-design ANOVA results indicated that perceived restorativeness remained stable across seasons, whereas emotional evaluations were significantly higher in autumn than in summer. Significant interaction effects between season and sex were observed in selected gaze metrics within the sky AOI, while the forest AOI showed a consistent main effect of sex across seasons. Spearman’s correlation analysis revealed a strong positive association between autumn PRS and SD scores, suggesting that aesthetic appreciation contributes to restorative perception. In addition, a significant negative correlation between forest and pond AOIs in autumn indicated a seasonal redistribution of visual attention. These findings highlight the importance of component-level landscape analysis and demonstrate that seasonal variation and user characteristics jointly influence perceptual and attentional responses in forest-healing environments. The results provide empirical implications for evidence-based forest landscape design and seasonal management strategies. Full article

This study examines the impact of family multidimensional poverty on cognitive function among older adults in China using the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Filling a critical gap in the existing literature, we construct a multidimensional poverty index (MPI) based on the Alkire-Foster methodology to evaluate cognitive decline within the context of China’s post-poverty-eradication landscape. Utilizing quantile regression analysis, our findings demonstrate that multidimensional poverty exerts a significant, negative effect on cognitive function, which is more pronounced among individuals at lower cognitive quantiles, consistent with the cumulative disadvantage theory. Furthermore, we identify substantial urban–rural and regional disparities, revealing unique socio-economic inequalities. By linking multidimensional poverty to elderly cognitive health through psychosocial pathways, this study provides empirical evidence that reducing multidimensional deprivation among older adults is integral to achieving both SDG1 and SDG3 in China’s post-eradication context, demonstrating that income-based metrics alone are insufficient to capture the full burden of poverty on elderly cognitive health. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article