Search Results (431)

Pedestrian Spatial Experience PSE in urban spaces is a multi-faceted topic that requires the thematization of assessment methods due to their fragmentation across studies. Accordingly, this systematic review followed an inductive approach to define a framework of PSE assessment themes reflecting their evaluation in public and university campus spaces. This systematic review included open-access, accessible, peer-reviewed sources based on assessment-focused English research that followed defined frameworks on the effects of urban environments on adult PSE. Studies were excluded if they focused on non-pedestrians or vulnerable user groups, examined non-pedestrian-scale contexts, explored pedestrian experience in virtual environments, assessed interior spaces, lacked a structured attribute-based assessment framework, were review articles, did not specify how urban environments shape pedestrian experience, investigated non-urban or rural areas, or examined urban settings without clearly defined street or square infrastructure. The review relied on querying PSE-related bibliography from the Scopus and Web of Science databases on 12 October 2025; results were processed through a screening procedure according to the inclusion and exclusion criteria. The final sets of sources reviewed included 83 and 24 sources related to PSE assessment in public and university campus spaces, respectively. Risk of Bias (RoB) tools included the Joanna Briggs Institute (JBI) tool for cross-sectional studies, tailored for urban spatial studies, and the Prediction model Risk Of Bias Assessment Tool (PROBAST+AI), tailored for ABM studies. Using a data extraction sheet and codebook to identify the prominent codes in the included sources, in addition to reviewing frequent words and the methods of the included sources, clarified the main conceptual framework of PSE assessment themes. The thematic categorization of PSE studies was followed by analyses of the frequencies of the themes, the prevalence of themes across countries and cities, and the theoretical explorations within the themes over the years in both reviewed contexts. Subsequently, synthesizing both sets clarified the interrelations between themes, methods, and tools as an attempt to address gaps in PSE assessment methods. The main results of this review are the 11 themes of PSE assessment that were identified from the reviewed sources. Data analyses and syntheses indicated a high prevalence of quantitative methods relying on visual aspects, signifying the dominance of the Cognitive and Navigational Experience theme due to its frequent assessment by numerous and diverse sets of methods in both reviewed sets. Nevertheless, the Temporal Experience theme emerged as the least considered. The key limitations of this systematic review include its reliance on accessible articles from bibliographic databases, as well as its focus on adult populations as the common users of public and university campus spaces. This review decodes PSE in terms of its assessment themes through the methods followed and the applied tools within real environments. As an application of the introduced conceptual framework, this systematic review clarifies the comparison of the themes examined between public and university campus spaces. The findings of this systematic review provide a foundation for a comprehensive understanding of PSE, thereby informing the design of more user-centered environments. Full article

Photovoltaic (PV) site suitability analysis has become an essential component of renewable energy planning due to the rapid global expansion of solar energy systems and the increasing complexity of land-use, environmental, economic, and infrastructural constraints. Over the past decade, Geographic Information Systems (GIS) integrated with Multi-Criteria Decision-Making (MCDM) techniques have emerged as the dominant methodological framework for identifying optimal PV deployment locations. However, recent advancements in machine learning (ML), explainable artificial intelligence (XAI), clustering techniques, and large language models (LLMs) are beginning to reshape the field toward more data-driven and intelligent spatial decision-making systems. This review provides a comprehensive analysis of PV site suitability studies published between 2016 and 2026, focusing on methodological evolution, criteria selection patterns, reproducibility challenges, and emerging AI-driven approaches. A systematic literature review was conducted using Scopus, Web of Science, and IEEE Xplore databases, resulting in 72 final studies after multi-stage screening. The analysis reveals that AHP-based GIS-MCDM frameworks remain overwhelmingly dominant, while machine learning and hybrid AI approaches are still limited but rapidly emerging. A total of 63 unique suitability criteria were identified, with climatic, infrastructure, and topographic factors representing the most frequently used categories. The review further highlights substantial challenges related to reproducibility, regional variability, data transparency, and expert-driven subjectivity. Recent studies employing explainable ML, unsupervised clustering, and LLM-assisted weighting frameworks demonstrate significant potential for improving adaptability, interpretability, and automation within renewable energy planning. The review concludes that future PV suitability analysis is likely to evolve toward hybrid GeoAI systems integrating GIS, ML, XAI, clustering, and human-centered AI frameworks to support more robust, scalable, and transparent spatial energy planning. Full article

Reliable precipitation forecasts are critical for hydrological modelling and flood early warning in West African river basins, where rainfall is dominated by highly variable monsoon-driven convection. This study evaluates and improves the precipitation forecasting skill of six numerical weather prediction (NWP) models over the Ouémé River basin in Benin, with particular emphasis on lead-time dependence, basin-scale effects, and the added value of statistical bias correction. Daily precipitation forecasts, over the period 1985–2015 across lead times of one to seven days, are assessed across six sub-basins using complementary continuous and event-based verification metrics. The results indicate that precipitation forecast skill varies with model choice, forecast horizon, and spatial scale. Among the raw forecasts, the ECMWF and UK Met Office models consistently outperform the other systems with KGE values reaching 0.5. ECMWF exhibits the highest overall skill at short to medium lead times, while the UK Met Office model shows relatively low volumetric bias across most sub-basins (Pbias less than 25%). For some models, forecast performance improves with increasing basin size, reflecting the smoothing effect of spatial aggregation, although this relationship remains model-specific. Distribution-based methods outperform regression-based approaches, with empirical quantile mapping providing the most robust and consistent improvements across lead times and sub-basins. Following bias correction, Empirical quantile mapping achieved median Likelihood Ratio values of approximately 6 during validation, with upper-range values reaching 15–18 across sub-basins for both ECMWF and UK Met Office forecasts. This represents a substantial improvement over raw predictions whose distributions remained consistently bounded below 10 throughout the calibration and validation phases (more than 50% improvement). Overall, the combination of ECMWF or UK Met Office precipitation forecasts with empirical quantile mapping offers a reliable framework for improving precipitation inputs to hydrological models and flood early warning systems in the Ouémé basin. The findings highlight the importance of multi-criteria evaluation and appropriate bias correction when applying NWP precipitation forecasts in monsoon-influenced hydrological environments and flood forecasting. Full article

The increasing complexity of marine spatial management and the rapid growth of nautical tourism require the use of formal and transparent decision-making models. Identifying optimal locations for nautical anchorages is a multi-criteria decision problem (MCDP) in which navigation safety, spatial constraints, and environmental protection often conflict. This study presents an integrated framework combining Geographic Information Systems (GIS) and multi-criteria decision-making (MCDM) methods for the systematic evaluation and ranking of nautical anchorages. As a case study, 86 potential locations in Split-Dalmatia County, Croatia, were analysed based on 18 criteria encompassing hydrological, meteorological, and spatial factors, as well as risk factors relevant to navigation safety. The methodological approach applies six MCDM methods implemented in the R programming language: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), ViseKriterijska Optimizacija I Kompromisno Rjesenje (VIKOR), Multi-Objective Optimisation on the Basis of Ratio Analysis (MOORA), Complex Proportional Assessment (COPRAS), Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), and Evaluation Based on Distance from Average Solution (EDAS). To reduce methodological bias, a final Consensus rank was calculated to synthesise the results of all applied methods. The stability of the obtained ranking was examined through an analysis of rank agreement between methods, using a diagonal matrix of rank overlaps and the corresponding heatmap visualisation. The results indicate a high level of consistency among individual MCDM methods and strong stability of the final consensus ranking. The proposed model ranks locations from best to worst based on how well they meet the established criteria, while ensuring strict navigational safety and compliance with environmental constraints. These findings confirm that the integrated GIS–MCDM approach is a reliable, repeatable, and scientifically grounded tool for supporting spatial planning and concession allocation in the development of nautical infrastructure. Full article

The safe disposal of spent fuel and high-level radioactive waste has become a critical bottleneck restricting the sustainable development of nuclear energy, and 3D geological modeling serves as a core technology for repository siting and safety assessment. Taking the upper member of the Lower Cretaceous Bayingobi Formation in the Tamusu area as the research object, this study focuses on sedimentary facies identification, lithofacies prediction, 3D geological modeling, and candidate site optimization. A convolutional neural network (CNN) + attention algorithm is proposed for high-precision lithofacies identification, and a Geo-CVAE-GAN model is constructed to address data sparsity and reconstruct 3D geological models. Following the workflow of single-well fine analysis, multi-method fusion prediction, and 3D geological modeling, the Sequential Indicator Simulation (SIS) algorithm is improved to build a 3D lithofacies model, and four-property parameter modeling is completed under facies control. Optimal sites are delineated via 3D spatial superimposition based on parameter thresholds. The results show that favorable mudstone layers display a dual-layer structure: stable thick layers in deep strata and thin superimposed layers in shallow strata. A preliminary total area of approximately 165 km² is identified in Preselected Sections I and II, with target intervals at a 400–800 m depth, mud content exceeding 75%, and excellent physical properties, including low porosity, low permeability, and low water saturation. This study reveals the spatial distribution of favorable mudstone in the Tamusu area, and the preferred zones fully meet the siting criteria for high-level radioactive waste repositories, providing a reliable geological basis and technical support for subsequent exploration and engineering design. Full article

Background/Objectives: Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability. Although convolutional neural networks (CNNs) have automated cervical cell classification, they remain limited in modeling long-range spatial dependencies and often lack clinical interpretability. Methods: In this study, Vision Transformer (ViT) architectures were systematically optimized to enhance automated cervical cancer screening and improve interpretability. The Herlev dataset (917 images: 242 normal, 675 abnormal) was utilized to optimize ViT-Tiny, a lightweight ViT architecture designed for reduced computational complexity, through a comprehensive evaluation of augmentation strategies, class weighting, and hyperparameters. Results: The optimal configuration achieved a cross-validation accuracy of approximately 95% (94.89% for the best replicated configuration), in which random horizontal flipping and class weighting (0.7 × 1.3) were identified as most effective. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirmed that model attention corresponded to clinically relevant morphological features, including nuclei regions, cell boundaries, and chromatin texture, which align with cytopathological criteria. Conclusions: These findings indicate that Vision Transformers can deliver accurate and interpretable decision support for cervical cancer screening by combining competitive classification performance with attention-based transparency relevant to medical AI. Further validation on larger, multi-center datasets remains necessary before clinical deployment. Full article

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality. Full article

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km²; equivalent to ≥10,000,000 m²) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km²; equivalent to 500,000–10,000,000 m²) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km²; equivalent to 10,000–500,000 m²) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database. Full article

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments. Full article

This study investigates the structural sensitivity of a large-span steel space frame at Yanjiao Station to environmental disturbances during the critical “flexible suspension” stage of asymmetric hydraulic lifting. First, by analyzing the offset between the center of mass and the center of stiffness—induced by the asymmetric lifting configuration—the study systematically examines the spatial eccentric amplification effect under a coupled thermal-wind field. To this end, a non-uniform solar radiation model based on the Axis-Aligned Bounding Box (AABB) algorithm is integrated with a refined finite element model, enabling a full-factor parametric analysis under 20 coupled load conditions. The results reveal a significant time lag in the structural temperature field, with 12:00 identified as the critical time for maximum thermal deformation. The wind-induced response follows a “bimodal evolution” pattern, and the maximum translational-torsional coupling effect occurs at wind direction angles of 60° and 120°. Further analysis of the multi-field coupling mechanism indicates that the wind field dominates the deformation mode, while the temperature field amplifies the resulting response. Consequently, the peak displacement reaches 192.50 mm, which represents a 360.81% increase compared to the dead load baseline. The cantilever end is identified as the primary vulnerable region. Based on these findings, a “wind direction–time” two-dimensional monitoring strategy is proposed. This strategy provides scientific quantitative criteria and theoretical support for the construction safety of large-span structures, as well as for the development of a comprehensive early warning and health monitoring system. Full article

Preventing large-scale illegal migration is one of the EU’s highest priorities. In this study, we analyze the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifically targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia’s external EU borders. The model’s methodological framework is based on the integration of Geographic Information Systems (GISs), Multi-Criteria Analysis (MCA), and the Analytic Hierarchy Process (AHP). The model utilizes various environmental and infrastructure variables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorized risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. The model was quantitatively verified using a weighted detection-versus-background design against 7481 geocoded border crossing incidents, demonstrating high predictive skill and robust calibration (Continuous Boyce Index up to 0.97) when controlling for patrol effort bias and spatial autocorrelation. High-resolution historical satellite imagery showing activities related to illegal migration was used for the generation of labeled datasets for AI training. Features such as suspicious vans, river boats, tire tracks, tents, illegal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applications, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimized operations. Full article

The rapid growth in vessel size associated with global maritime trade is placing increasing pressure on port infrastructure worldwide. In Brazil, many existing ports face structural limitations due to insufficient navigational depth and limited opportunities for spatial expansion, often constrained by urban encroachment. In this context, identifying suitable coastal locations for deep-draft port development has become a key strategic challenge for long-term planning. This study develops a GIS-based spatial suitability model to identify segments of the Brazilian coastline with favourable conditions for deep-draft port infrastructure capable of accommodating large vessels, including post-Panamax ships. The approach considers physical constraints, environmental restrictions and basic logistical connectivity within a multi-criteria spatial framework implemented through map algebra. The model is conceived as a strategic screening tool to support early-stage decision-making rather than a detailed feasibility assessment. The results identify nine coastal locations with the highest suitability scores, indicating that highly favourable conditions for deep-draft port development are spatially limited. Notably, one of these candidate locations partially overlaps with an existing port-related cluster, suggesting consistency between the model outputs and real-world port development patterns. In contrast, large portions of the southeastern coastline (particularly in São Paulo and Paraná) exhibit lower suitability due to a combination of urban pressure, environmental constraints and limited depth conditions. Overall, the findings reveal a spatial mismatch between Brazil’s main economic core and the coastal areas with more favourable natural conditions for new port infrastructure. The proposed framework contributes a transparent and transferable spatial decision-support tool that can assist policymakers in identifying priority areas for future port development and in balancing investments between the expansion of existing ports and the development of new locations. Full article

Digital infrastructure expansion in Europe raises a spatial planning problem: early-stage screening needs to compare regional conditions while also checking whether rankings remain stable when decision priorities change. This study evaluates 24 European Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) regions for geo-distributed data center development. The 2022 decision matrix uses five Eurostat criteria: information and communications technology (ICT) specialists’ share in employment, average hourly labor cost, renewable electricity share, non-household electricity price and population density. Four criteria are national intensive proxies assigned to the selected NUTS-2 regions, while population density is directly observed at the NUTS-2 level. After a log₁₀ transformation of population density and min–max normalization, we compare the weighted sum model (WSM), TOPSIS and VIKOR across four weighting scenarios. We then apply a random-weighting audit based on Stochastic Multicriteria Acceptability Analysis (SMAA) principles, using 10,000 Dirichlet weight draws, followed by a local Dirichlet sensitivity analysis around the Balanced profile. Results show that the most stable high-performing profiles are not limited to the established FLAP-D market reference. Latvija (LV00), Stockholm (SE11), Helsinki-Uusimaa (FI1B), Eesti (EE00) and Área Metropolitana de Lisboa (PT17) form the main high-performing set across stochastic rank metrics, while several mature Western metropolitan regions remain more sensitive to cost and territorial-pressure criteria. The study provides a reproducible spatial decision support framework for macro-regional screening rather than micro-siting. Full article

Soil erosion in arid watersheds poses a significant threat to land productivity, water resources, and long-term sustainability, necessitating spatially explicit and data-driven prioritization frameworks for targeted conservation. This study developed an integrated GIS-based multi-criteria decision-making (MCDM) framework to assess soil erosion susceptibility and prioritize twelve sub-basins (SB) of the Wadi Numan basin (683 km²), Makkah Region, Saudi Arabia. Morphometric analysis was conducted using sixteen parameters derived from a 10 m Digital Elevation Model (DEM), and Land Use/Land Cover (LULC) data were obtained from the Esri Sentinel-2 10 m dataset. Four MCDM techniques—additive ratio assessment (ARAS), complex proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and technique for order preference by similarity to ideal solution (TOPSIS)—were applied under the criteria importance through inter-criteria correlation (CRITIC) objective weighting, and their consistency was evaluated using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). MOORA achieved the highest consistency for morphometric analysis (SCCT: 0.982; KTCCT: 0.958), while TOPSIS performed best for LULC analysis (SCCT: 0.800; KTCCT: 0.731). The final combined prioritization used MOORA for morphometric analysis and TOPSIS for LULC analysis, with proportional weighting of 72.7% and 27.3%, respectively. The scheme categorized the sub-basins into five levels of soil erosion priority. The composite ranking classified SB-9 and SB-1 under very high priority (25.94%); SB-2 and SB-3 under high priority (6.40%); SB-5, SB-6, and SB-10 under medium priority (36.37%); SB-4 and SB-8 under low priority (18.11%); and SB-11, SB-12, and SB-7 under very low priority (13.18%). This integrated method provides a practical decision-support tool for identifying and managing sub-basins susceptible to soil erosion, thereby promoting the long-term sustainability of land and water resources. Full article

Soil quality is central to agricultural sustainability and food security, yet coastal agroecosystems are increasingly threatened by degradation from intensive practices and seawater intrusion. This study aimed to integrate soil quality index (SQI), statistical modeling, machine learning (ML), and decision analysis to assess and manage soil health in the Skhirat coastal plain of Morocco. A total of 30 topsoil samples were collected and analyzed for chemical and nutrient properties. Spatial interpolation revealed strong coast–inland gradients where EC ranged from 0.47 to 6.3 dS/m with the highest salinity in the south-western fringe, while CEC (8.4–39.7 cmol/kg) and OM (0.54–2.81%) peaked inland. Principal component analysis (PCA) explained 65.9% of total variance, with salinity drivers loading negatively against fertility indicators. Redundancy analysis (RDA) biplots highlighted antagonism between salinity and fertility axes. The PCA-minimum data set (MDS)-SQI integrated key indicators and ranged from 0.084 to 0.897 (mean 0.614), classifying 33% of sites as low quality. The ML model linear regression achieved the best performance (R² = 0.907). Multi-criteria decision analysis (MCDA) using TOPSIS and PROMETHEE II prioritized coastal sites with indices up to 0.882, and robust underweight sensitivity (Spearman ρ = 0.992). This integrated framework demonstrates that soil chemical monitoring, AI prediction, and MCDA can jointly deliver robust, site-specific management strategies for vulnerable coastal agroecosystems. Full article