Search Results (2,703)

Starting from a semiotic understanding of the city as a stratified and continuously rewritten palimpsest, this paper investigates the relationship between urban heritage, graphic representation, and cultural translation within the current digital scenario. The study introduces and develops a theoretical framework, the “translation triangle” (TT), articulated around three mutually co-determining domains: the cognitive, concerned with the critical identification of relevant signs within the urban text; the technical–digital, concerned with their formalisation through survey instruments, three-dimensional modelling, and visualisation pipelines; and the socio-anthropological, concerned with the mediation of translated content towards its recipient. The framework is tested through two urban contexts: Pavia, Italy, and Gdańsk, Poland, different in scale and research advancement. Both demonstrate that the quality of urban heritage representation depends not solely on geometric accuracy or visual effectiveness, but on the capacity to make explicit the interpretative choices embedded in each translational act and to produce forms of knowledge that are comprehensible, interrogable, and culturally shareable. The paper concludes that every digital representation of urban heritage constitutes a semiotic act, not a neutral technical operation, and that the future of the discipline lies in constructing translations that are conscious, verifiable, and culturally grounded, capable of preserving the historical meaning of heritage without reducing it to spectacle or data infrastructure. Full article

The Mediterranean region faces severe water scarcity exacerbated by climate change. The reuse of treated industrial wastewater for industrial processes or agricultural irrigation offers a sustainable mitigation strategy, yet its implementation is hindered by low social acceptance and institutional fragmentation. This study aims to enhance treated wastewater reuse (TWWR) uptake within the textile and pharmaceutical sectors in Türkiye and Tunisia through structured stakeholder engagement, hydrological modelling, simulation, and co-design of preferred strategies. Data were collected from regional water authorities, industries, farmers, and civil society, chosen based on their influence, interest, and capacity regarding integrated water management. A Participatory Adaptive Planning framework was applied across three co-designed workshops per site, and interviews. Data were charted using the Natural Assurance Schemes Business Canvas and a conceptual visual support framework. Stakeholders identified key barriers, including legislative gaps in Türkiye and financial capabilities and farmer reluctance in Tunisia. Through the conceptual visual support framework, holistic strategies combining TWWR, water savings, urban treated wastewater quality improvement by tertiary treatment, desalination, and Managed Aquifer Recharge were co-created, simulated and ranked. Technological innovation in wastewater treatment is insufficient without robust “social readiness”. Participatory governance effectively aligns environmental engineering solutions with societal needs, establishing a foundation for resilient and integrated water management action plans. Full article

Rapid urban population growth has intensified traffic congestion in smart cities. This has resulted in longer travel times, higher fuel consumption, increased environmental pollution, greater operational costs, and slower emergency response services. Existing traffic congestion prediction models primarily rely on traffic-flow and temporal features; the effects of road accidents and peak-hour conditions are not adequately addressed. This limitation is particularly significant in smart cities where both recurrent congestion (peak-hour demand) and non-recurrent congestion (road accidents) influence traffic conditions they have a significant impact on the performance of the road network. This study introduces a novel Historical Accident-Aware Peak-Hour GAN-GRU (APG-GRU) framework. The proposed framework employs a data processing pipeline integrating traffic data with historical accident-related features to predict traffic congestion using these features. Extensive experiments are conducted on a novel integrated dataset consist on Automatic Number Plate Recognition (ANPR) traffic data and ANPR traffic observations with historical accident features. The results demonstrate that the APG-GRU framework achieved superior performance on the integrated features dataset, attaining an accuracy of 97.50%, a congested precision of 91.86%, a congested recall of 97.31%, and a congested F1-score of 94.51%, outperforming both the ANPR traffic-only dataset and all baseline models. The APG-GRU framework significantly outperforms a suite of benchmark models, including XGBoost, Long Short-Term Memory (LSTM), and Random Forest as baselines, which achieved accuracies between 84% and 95.5% with correspondingly lower precision, recall, and F1-scores. External validation using a traffic dataset collected from Lahore, Pakistan, further demonstrated the robustness and generalizability of the proposed APG-GRU framework. A web-based interface developed for the APG-GRU framework to visualize accident hotspots and route-level traffic conditions. Routes with smooth traffic flow are highlighted in green, whereas congested routes are highlighted in red, demonstrating the practical applicability of the proposed framework for smart city traffic management systems. Full article

With the rapid development of remote sensing technology, high-resolution satellite imagery has been increasingly applied to land resource monitoring, urban planning, and environmental assessment. Automatically assigning semantic labels to remote sensing image patches remains a fundamental challenge due to pronounced intra-class variation and high inter-class visual similarity. To address the trade-off between model capacity and limited labeled data, this paper proposes a remote sensing image scene classification framework based on an improved EfficientNetV2-S architecture. The proposed model integrates a Squeeze-and-Excitation (SE) channel attention module between the final 1 × 1 expansion convolution and the Global Average Pooling layer, where it functions as a late-stage channel gating mechanism that adaptively recalibrates channel-wise responses, though its accuracy benefit is seed-sensitive rather than consistently reproducible at the current dataset scale. A two-stage optimization strategy was evaluated, comprising a fully unfrozen supervised baseline followed by a pseudo-label semi-supervised fine-tuning stage utilizing a strict confidence threshold ($\tau =0.90$). Evaluated on a 10-class subset of the public NWPU-RESISC45 benchmark, the purely supervised SE-EfficientNetV2-S delivers 98.71% independent test accuracy, matching or exceeding the much larger ResNet50 (98.50%, 24.1 M parameters) despite using only 20.4 M parameters. Multi-seed variance analysis further reveals that semi-supervised fine-tuning yields a small test-set improvement for the No-SE configuration that is consistent in sign across all three seeds (+0.46 pp mean) but not statistically significant at this sample size, and an even smaller, likewise non-significant gain for the SE-augmented model (+0.08 pp), suggesting that channel gating moderates pseudo-label effectiveness in small-data regimes. Full article

As essential components of urban green spaces, pocket parks play a critical role in enhancing residents’ mental restoration in high-density cities. Taking six representative pocket parks in Guangzhou as a case study, this study developed a 16-indicator evaluation framework based on Perceived Sensory Dimension (PSD) theory. The supply–demand balance of restorative environments in pocket parks was investigated via questionnaire surveys (n = 276, primarily aged 18–45 of the urban working population), expert assessments, and Importance–Performance Analysis (IPA). Results showed that respondents strongly prioritized “vegetation coverage,” “spatial openness,” and “safe stayability.” Furthermore, the high–stress group exhibited higher preferences for specific restorative attributes (e.g., “spatial openness,” “undisturbed environment,” and “cultural expression”). The IPA results revealed a structural supply–demand mismatch: High-restorative-benefit attributes such as “spatial openness” and “visual permeability” fell into the low-performance quadrant, requiring urgent improvement, whereas lower-benefit features such as “public activities” showed relatively high performance. Based on these findings, this study proposes targeted optimization strategies to guide the health-oriented renewal and sustainable management of pocket parks and to provide empirical evidence for evidence-based design of urban green space systems in high-density cities. Full article

Digital Twins (DT) are increasingly positioned as key enablers of sustainable urban development, yet many implementations remain fragmented, technology-driven, and weakly connected to clearly defined decision-making needs. The present study develops a structured information governance framework for DTs, drawing on the principles of ISO 19650. The framework establishes a traceable hierarchy linking organizational objectives, DT use cases, information requirements, the Level of Information Need, information exchange processes and machine-readable Information Delivery Specifications. Its purpose is to ensure that information is clearly defined, exchanged, validated, and maintained in a consistent and verifiable manner before it is used for monitoring, simulation, predictive analytics, or decision support. The proposal is illustrated through an urban air-quality Digital Shadow demonstrator integrating BIM, GIS, weather services, and a real-time visualization environment. Candidate information-quality indicators are also introduced and demonstrated through synthetic calculations intended to explain their application. Neither the demonstrator nor the calculated KPI values constitute validation of the framework or evidence of improved operational performance. Instead, they establish a structured basis for future testing in operational Urban Digital Twin (UDT) implementations. The contribution lies in integrating established BIM concepts into a single DT-oriented traceability chain rather than introducing them as new standards or methods. Full article

Digital street-view imagery delivers computationally enabled fine-grained quantitative perception of urban landscape, supporting the refined assessment of urban spatial quality, ecological livability, and socio-economic externalities for socio-environmental sustainability. Taking Shanghai as the research area and 500 m × 500 m grid cells as basic analytical units, this study adopts the Segformer machine learning segmentation algorithm to accurately extract visual features of urban green infrastructure from massive Baidu street-view images. Combined with visitor volume and real estate transaction data, this paper systematically explores vitality effects and sustainable economic compensation of Green View Index (GVI) via the spatial hedonic model. Multi-model comparison verifies that the double-logarithmic framework is optimal for street-view visual data. Two empirical models are constructed to eliminate multicollinearity and differentiate effects of integrated built environments and segmented visual elements. The results indicate that Shanghai’s vitality presents a polycentric agglomeration pattern, while GVI shows a scattered spatial distribution, with strong spatial correlation in urban cores and weak linkage in suburbs. GVI acts as a determinant of block vitality, outperforming land use diversity and commercial density. A 1% increase in the GVI improves block vitality by 6.21% in C gradient, with positive effects concentrated on outer-ring green belts and inhibitory impacts in remote suburbs. Multi-scale analysis from 500 m to 5000 m confirms green optimization brings stable residential premiums, generating sustainable economic compensation to offset urban renewal costs. This study proposes a digital-imagery-based paradigm for GVI benefit evaluation and provides empirical evidence for the planning of ecologically sustainable communities. Full article

Urban fragmentation represents one of the major challenges affecting post-socialist residential neighborhoods, generating underutilized spaces characterized by ecological degradation and reduced spatial cohesion. This study investigates the potential of ornamental and aromatic plants to support the regeneration of apartment courtyard spaces and their integration into urban green infrastructure systems through the evaluation of a proposed landscape design scenario. A comparative approach was employed, combining Space Syntax analysis, expert-based ecological assessment, and perceptual–visual evaluation to compare the existing site conditions with the proposed landscape design scenario in a post-socialist residential neighborhood in Craiova, Romania. Spatial configuration was assessed using the indicators of integration, connectivity, and mean depth, while ecological and perceptual performance were evaluated through biodiversity-related indicators and the Scenic Beauty Estimation (SBE) method based on expert assessments. The proposed intervention, founded on a stratified composition of ornamental and aromatic vegetation, reduced impervious surfaces and was associated with improvements in spatial permeability, biodiversity potential, and the visual quality of the landscape. Space Syntax analysis indicated an increase in configurational performance (ΔC ≈ +41.6%), while perceptual evaluation showed higher mean expert ratings for aesthetic, functional, and sustainability attributes in the proposed design scenario compared with the existing conditions. These findings suggest that, within the limits of this case study and the applied evaluation framework, small-scale green interventions based on ornamental and aromatic plants may contribute to improving the ecological and spatial performance of fragmented urban spaces and provide a methodological framework for assessing similar regeneration projects in post-socialist residential contexts. Full article

Building facades are highly exposed envelope components whose degradation affects durability, habitability, urban image, and maintenance planning. Several studies address facade anomalies and service-life prediction. However, a need remains for integrated, reproducible procedures that combine visual inspection, taxonomic classification, and statistical analysis within a single framework. This research develops and validates a standardized methodology for the assessment of facade pathologies in urban buildings. The proposed framework is structured into sequential phases: documentary research, systematic visual inspection, photographic recording, damage classification, facade mapping, standardized inspection sheets, database generation, statistical analysis, and cluster-based interpretation of damage patterns. The methodology was validated through an urban case study in Valencia, Spain, where 168 building facades were inspected and 1600 damage were identified, classified, mapped, and digitized. The collected data were analysed according to building age, environmental exposure, and affected facade units. Soiling due to differential washing was the most frequent damage type, with 295 cases. Buildings constructed between 1930 and 1960 concentrated the highest number of recorded cases (639), while the wall area near ground level was the most affected facade unit (499 cases). K-means analysis retained a three-cluster solution, with a Silhouette Score of 0.65 and a BSS/TSS ratio of 86.13%. In addition, K-means cluster analysis was applied to classify damage types according to their frequency after Z-score standardization and validation through Silhouette Score and BSS/TSS metrics. The results demonstrate that the proposed framework enables homogeneous data collection, reproducible classification, and diagnostic interpretation of recurrent facade damage. Beyond the specific findings of the Valencia case study, the main contribution of this work is the development of a transferable assessment framework that can support preventive maintenance protocols, inspection planning, and evidence-based conservation strategies in other urban contexts. Full article

This study analyzes the relationship between verbal interaction and eye behavior among 112 primary school students in urban and rural classrooms in Chile, using wireless eye-tracking technology. The results reveal statistically significant differences based on socioeducational context and sex. Linear regression analyses show that gaze is a significantly more robust predictor of class participation in rural contexts (R² adjusted = 0.671) than in urban contexts (R² adjusted = 0.342). Furthermore, eye behavior explained 71% of the variance in male students, compared to 37.7% in female students. While female students focused their attention primarily on teachers, male students relied on a shared visual distribution between the teacher and peers to regulate their participation in class. In conclusion, the gaze acts as a differentiated scaffolding whose importance intensifies in boys and rural environments. These findings suggest distinct maturational trajectories that require teachers to implement visually intentional instructional strategies to ensure communicative efficiency in the classroom. Full article

Under rapid urbanization and watershed-scale spatial restructuring, urban green-space systems are often treated as residual indicators after construction land has been allocated, which limits the capacity of blue–green networks to act as leading frameworks for spatial structure and development sequencing. Taking the Sanjiangkou New Town Master Plan in Lishui, Zhejiang Province, China, as a case, this study develops the concepts of landscape-flow transformation and locality in urban green-space networks and examines their generative, planning, and participatory mechanisms through planning-document interpretation, visual evidence-chain analysis, sequential scenario construction, and an exploratory public-participation questionnaire survey. The paper proposes an integrated Perception–Cognition–Interaction (PCI) and Ecology–Construction–Program (ECP) framework. The ECP framework clarifies how locality-based landscape ecology constrains network formation, how frontloaded green networks shape urban zoning and mobility structures, and how long-term construction, use, and feedback refine master-planning schemes. The PCI framework explains how the public enters planning communication through embodied locality perception, structural understanding, and interactive feedback. Based on 400 valid questionnaires, the results reveal significant differences between local and non-local respondents in locality perception and planning understanding. The PCI pathway provides exploratory evidence that perception, cognition, and interaction are closely associated in scenario-based planning communication. The study argues that green-space networks should be introduced as an ecological substrate, structural constraint, and dynamic feedback system rather than as post hoc environmental land-use allocation. Its contribution is to reposition locality from a visual character label to a mechanism of pattern generation, phasing, and participatory negotiation. Full article

Digital device use has substantially altered children’s visual environments, yet the specific behaviors associated with axial elongation and early retinal remodeling remain unclear in highly urbanized settings. This study investigated biological and behavioral correlates of axial length (AL) and associated macular microstructural changes in preadolescent children. Participants underwent ophthalmic examinations and completed questionnaires assessing lifestyle and digital device use factors. Multivariable linear mixed-effects models identified factors associated with AL and macular thickness. The participant-level prevalence of myopia, defined as spherical equivalent ≤ −0.50 D, was 53.2% and increased from 37.1% in Grade 1 to a peak of 78.7% in Grade 4. Older age, a greater number of myopic parents, flatter corneal curvature, male sex, continuous smart device use exceeding 1 h per session, and viewing distance < 30 cm were independently associated with longer AL. Each 1 mm increase in AL was associated with significant subfoveal choroidal thinning and generalized extrafoveal macular thinning, whereas the central fovea remained relatively preserved. Pediatric AL was associated with hereditary susceptibility, specific digital viewing behaviors, and early extrafoveal retinal structural variation. Full article

UAV-borne visual sensors provide high-resolution aerial observations for low-altitude scene understanding, urban monitoring, traffic observation, emergency inspection, and infrastructure assessment. However, semantic perception from UAV visual sensor data remains challenging because aerial images often contain dense small objects, elongated road structures, fragmented boundaries, scale variations caused by flight-altitude changes, oblique viewpoints, and strict onboard or edge computational constraints. To address these challenges, this paper proposes MEKD-UAVSeg, a lightweight semantic perception framework based on conflict-suppressed heterogeneous expert distillation. During training, a Transformer-based semantic expert provides global contextual understanding and region-level class consistency, while a Mamba-based spatial expert provides complementary structural guidance for roads, roofs, boundaries, and other continuous aerial structures. Both experts are used only during training, and the final inference model remains a compact CNN-based segmentation network. In addition, UAV-aware density and hard-region priors are designed to emphasize small-object-dense areas, boundary-sensitive regions, rare classes, and uncertain aerial categories. A conflict-suppressed reliability routing strategy is further developed to reduce inconsistent supervision between heterogeneous experts and selectively transfer reliable knowledge to the student model. Experiments on UAVid and UDD6 demonstrate that the proposed framework achieves a favorable accuracy–efficiency trade-off compared with representative CNN-, Transformer-, Mamba-, and hybrid-based UAV segmentation methods, without introducing expert-induced inference complexity. Full article

The route to wellbeing is often divided into hedonic (pleasure or relaxation) and eudaimonic (meaning or growth) pathways. Positive technology is a growing research area which aims to use technology to facilitate engagement in wellbeing-supporting activities. While virtual reality (VR) is increasingly used to support day-to-day wellbeing, the mechanisms underlying these effects remain unclear and VR research typically determines success based on outcomes. This leaves a gap whereby the processes which facilitate outcomes are less understood. This exploratory study examined the relative effects, on participant emotion responses (N = 35), of hedonic (nature vs. urban setting) and eudaimonic (personally meaningful vs. not personally meaningful) VR environments. Results showed that the level of personal meaning associated with an environment influenced emotion outcomes and visual setting did not. Notably, meaningful environments elicited a “mixed-emotional” state, increasing both positive and negative emotional responses. The results highlight the potential for short, personalised virtual experiences to elicit emotionally complex responses that are theoretically consistent with eudaimonic processes like meaningful reminiscence. Results are discussed in relation to the relative influence of hedonic and eudaimonic stimuli to be explored in future research that builds on these findings. Full article

In response to Saudi Arabia’s amended White Land Fees Law, which imposes charges of up to 10% of land value on undeveloped urban plots, this study presents TerraVision, an intelligent framework for large-scale White Land detection and urban land monitoring using high-resolution satellite imagery and deep learning. The proposed framework aims to support sustainable urban development by enabling municipalities and planners to identify underutilized urban land, improve land-use efficiency, and support evidence-based planning decisions. Satellite imagery was acquired through the Esri ArcGIS platform at a spatial resolution ranging from 0.31 to 0.34 m per pixel. The Riyadh study area was divided into 1317 geographic tiles, of which 80 tiles covering approximately 180 km² were manually annotated to construct the training and evaluation dataset. Ten segmentation models representing four architectural families were evaluated, including encoder–decoder networks, transformer-based architectures, YOLO segmentation models, and the zero-shot Segment Anything Model 3 (SAM3). Six fine-tuned semantic segmentation models achieved Intersection over Union (IoU) scores between 0.94 and 0.96 on the held-out test set, with SegFormer achieving the highest performance at an IoU of 0.9563. A post-inference geoprocessing pipeline was developed to reconstruct city-scale prediction maps, estimate neighborhood-level White Land availability, and export results into GIS- and web-compatible formats. The framework was further integrated into a bilingual (Arabic and English) decision-support dashboard that enables visualization and spatial analysis of vacant land distribution. The results demonstrate that semantic segmentation models provide an accurate solution for monitoring undeveloped urban land that scales to city-wide inference across Riyadh, and can support preliminary screening for strategic urban planning and sustainable city development initiatives in Riyadh. Full article