Search Results (507)

The 1960s to early 1990s marked a pivotal era in Kazakhstan’s mass housing history, particularly in Almaty, which was driven by a sector-wide policy shift toward apartment-based occupancy, regulated space standards, full utility provision, and organised residential courtyards. Large-panel buildings from this period still comprise over one-third of Almaty’s housing stock, yet their alignment with modern comfort standards remains understudied. This study establishes a historical periodisation of Almaty’s large-panel housing and assesses its comfort levels at apartment and built-environment scales. The two-part methodology combined a theoretical strand (comfort-level theory, literature review, historical retrospective, and periodisation) with an analytical strand (comparative graphical-analytical assessment of series 1Kz-464-AS, 1Kz-464-DS, 69, E-147 and 158), converging in a Basic–Supplementary–Advanced comfort classification. The results of the current study showed that three sub-periods emerged. Series 1Kz-464-AS and 1Kz-464-DS used a narrow-bay (2.6–3.2 m), economy-driven two-bay system with minimal zoning. Series 69 widened bays to 3.6–5.4 m, improving spatial comfort and utility provision. Series E-147 and 158 introduced a three-bay scheme with an added 2.1 m bay and raised ceilings (3.0 m), yielding the most flexible layouts. At the urban scale, microdistrict planning enlarged planning units by 7–10 times, preserved green connectivity through mid-rise buildings, and shielded courtyards using perimeter high-rises, supporting walkable, infrastructure-rich neighbourhoods. Apartment-scale comfort improved incrementally through bay widening rather than layout diversification, leaving persistent redevelopment constraints from panel structures. Built-environment comfort, however, reached a comparatively advanced, durable standard. Adaptive reuse should prioritise structural refurbishment, reduced occupancy density, courtyard–microdistrict continuity, and diversified public-space functions. This study’s results can inform renovation strategies for Soviet-era housing across post-Soviet cities. Full article

Light exposure in urban street environments significantly shapes pedestrians’ subjective perception. While previous studies based on street view imagery (SVI) have predominantly regarded sky regions as the sole source of such exposure, glazed facades also contribute significantly through reflected light. Taking the Tianhe District of Guangzhou, China, as the study area, this research utilized luminance extraction and deep learning techniques to process SVIs and constructed an interaction regression model to analyze the channels through which light exposure affects pedestrian perception, as well as the specific role of glazed facades in this process. Key findings include: (1) elevated light exposure negatively affects overall perception, serving as a critical constraint on environmental quality; (2) under equivalent exposure conditions, glazed facades, although their overall main effect on perception remains negative, can partially buffer the perceptual burden induced by sky-dominated exposure; (3) based on these empirical channels, we propose classification and targeted renewal strategies for streets. This study proposes an integrated framework comprising “Sky/Glazed Facades—Light Exposure—Perception”, successfully incorporating reflected light from glazed facades into SVI-based luminous environment research. It reveals the differentiated channels of light exposure effects, providing guidance for both building forms and facade materials across urban blocks. Full article

Accurate identification of land subsidence types is essential for effective urban risk management, yet remains challenging due to the superposition of deformation signals at different spatial scales and the complexity of urban environments. In this study, land subsidence types in Fuzhou were identified through an integrated framework combining multi-scale deformation analysis and ensemble learning. Ground deformation time-series measurements were derived from 66 Sentinel-1A synthetic aperture radar (SAR) observations acquired between January 2018 and June 2023 using the time-series interferometric synthetic aperture radar (TS-InSAR). Deformation values in decorrelated areas were subsequently reconstructed using regression models driven by multi-source geological, hydrological, land-use, and urban features, resulting in a spatially continuous deformation field. A Fast Fourier Transform (FFT)-based Butterworth filtering approach was then applied to separate regional-scale and local-scale subsidence signals. Based on the extracted local deformation patterns and discriminative auxiliary features, land subsidence was classified into five categories: farmland-related subsidence, linear infrastructure-related subsidence, low-lying stratum-related subsidence, land-use transition-related subsidence, and older building area-related subsidence. Three ensemble learning models, XGBoost, CatBoost, and LightGBM, were implemented for subsidence type classification. All models achieved satisfactory performance, among which LightGBM exhibited the best overall performance. The classification results reveal pronounced differences in spatial distribution and deformation intensity among subsidence types. Farmland-related subsidence occupies the largest proportion of the affected area but is characterized by relatively moderate deformation rates, whereas older building area-related subsidence, despite its limited spatial extent, exhibits the highest deformation intensity. This study demonstrates the potential of ensemble learning for land subsidence type classification. Full article

As population aging accelerates, providing psychosocial support for adults in the retirement transition has become increasingly important. For this population, a central challenge is adapting to changes in social roles, daily routines, and social relationships, yet existing social robot research has paid insufficient attention to these companionship-related needs. From an embodied cognition perspective, this study developed a needs-driven design pathway for a social companion robot for this population in urban China. Sixteen key needs were identified through user interviews and prioritized through a Kano survey with 184 valid responses from urban community-dwelling adults in the retirement transition. Based on the classification and prioritization results, these needs were synthesized into four design strategies: emotional responsiveness and trust building, social connectedness and sustained engagement, low-burden interaction and daily life support, and safety, health, and privacy protection. Privacy and safety were treated as foundational conditions for product design and practical deployment. The strategies were subsequently mapped to technical features and hardware elements through quality function deployment (QFD), using an expert-panel evaluation procedure and sensitivity analysis to determine module-level configuration priorities. A single-session, laboratory-based concept evaluation was conducted with 50 participants using concept renderings and interaction-flow videos. Concept 3, whose overall configuration emphasized coordinated voice, screen-based visual, and expression/action feedback, received a significantly higher mean participant-level Behavioral Intention (BI) score than Concept 2, which placed greater emphasis on spatial mobility assistance through a mobile wheel module (8.94 ± 0.97 vs. 8.51 ± 1.21; adjusted p = 0.003). These evaluation findings informed the final concept configuration and provided preliminary support for its companionship-oriented multimodal interaction approach. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Recent destructive earthquakes have clearly demonstrated that damage distribution in many cities developed on thick alluvial deposits is strongly controlled by local soil amplification and site response effects. Soil conditions therefore play a critical role in determining the characteristics of ground motion and the seismic performance of structures during earthquakes. This study presents the first microzonation-oriented site response assessment for the rapidly urbanizing city of Afyonkarahisar, which is characterized by thick alluvial deposits and a shallow groundwater table. A database consisting of 124 boreholes was compiled to characterize the subsurface stratigraphy of the study area. Shear-wave velocity profiles were verified using both SPT-based correlations and MASW measurements to ensure reliable input parameters for dynamic analyses. One-dimensional equivalent linear and nonlinear site response analyses were performed using the DeepSoil program, employing eleven earthquake ground motion records scaled according to the Turkish Building Earthquake Code. The results indicate that for Earthquake Level-1 (EL-1; 2% probability of exceedance in 50 years) ground motions, nonlinear analyses produce lower amplification factors (1.00–1.62), whereas equivalent linear analyses tend to predict higher amplification values, reaching up to 4.52, owing to their simplified treatment of soil nonlinearity. Under Earthquake Level-2 (EL-2; 10% probability of exceedance in 50 years) motions, both methods yield comparable amplification values ranging from 1.18 to 1.72. GIS-based amplification maps reveal significant spatial variability within the study area and identify zones where local soil conditions may substantially increase seismic demand. The findings suggest that nonlinear site response analysis is more appropriate for representing soil behavior under strong ground motions (EL-1), while both approaches provide comparable results for moderate ground motions (EL-2). Comparisons with Eurocode 8 and NEHRP site classifications further confirm the broader applicability of the results. Overall, this study provides a practical framework for reliable site response assessment that supports earthquake-resistant design and microzonation studies in seismically active regions characterized by complex alluvial environments. Full article

Building materials and their spatial distribution play a significant role in determining the outcomes of human-caused and natural disasters in urban and peri-urban areas. However, building-level data on building and roofing materials are scarce. Here, we explore the feasibility and performance of a Convolutional Neural Network (CNN) model using spectrally transformed high-resolution multispectral imagery to map roofprints (i.e., classifying and delineating roofing materials at the building footprint-level) in Washington, District of Columbia (D.C.) and Denver, CO, United States. To generate consistent training data, we integrate geospatial vector data of individual building footprints with real estate industry-derived building-level roofing material data to create labeled image data from Planet SuperDove imagery. We compare the CNN classifier to a pixel-based machine learning (ML) model to demonstrate the capability of our roofprints mapping approach. With F1-scores ranging from 0.56 to 0.95 for the most common roof material classes, the CNN model outperformed the pixel-based ML classifier by 15% and 17% in Washington, D.C., and Denver, respectively. Results demonstrate within-domain robustness for the studied metropolitan areas, which are characterized by differing building densities, roof morphologies, and material patterns. While cross-region transferability was not evaluated, our findings provide a controlled comparison of pixel-based and context-aware approaches for rooftop material mapping and highlight the importance of hierarchical representations that integrate spectral information with roof texture, edge characteristics, spatial arrangement, and neighborhood context for improving classification performance. Accurately mapping building materials has the potential to advance urban planning and environmental policies, including assessments of heat exposure, energy demand, as well as hazard risk and community resilience. Full article

Rapid urbanization has intensified urban heat issues. Previous studies often relied on subjective block selection and rarely integrated vegetation data. This study extracted vegetation from Wuhan’s satellite imagery and combined it with building geometry to generate large-scale 3D block models. Typical blocks were identified by clustering, and thermal environments were simulated using ENVI-met to establish regression models. POI and spatial analyses validated the results. The study found that 1. t-SNE outperforms PCA and UMAP in dimensionality reduction. 2. K-means surpasses GMM, DBSCAN, and Spectral in clustering. 3. ${SVF}_{ave}$, ${FA}_{all}$, ${VD}_W$, $VAR$, and BBA are critical for block morphology classification and block outdoor thermal assessment. 4. The final ridge regression model based on these indices achieved high R² values (0.805, 0.507, and 0.855), indicating excellent model performance. 5. The blocks in Cluster 1 (west of the Yangtze River) exhibit higher mean air temperatures. 6. the blocks in Cluster 2 (new areas) have high vegetation coverage, causing larger temperature differences between the inside and outside of blocks. This study provides a comprehensive workflow for urban block morphology classification and thermal assessment. Full article

The paper addresses the intersection between modern built heritage preservation and Climate-Aided Design (CADe) processes in fragile coastal Mediterranean contexts. The study focuses on the city of Beirut in Lebanon, part of the Eastern Mediterranean and Middle East (EMME) region and considered a climate change hotspot facing extreme challenges. Rapid urbanization and socio-political instability, especially during the twentieth century, have undermined the city’s ability to mitigate and adapt to future climate change scenarios. Moreover, Beirut’s modern built heritage faces a constant threat of demolition due to the absence of protective legislation, compounded by aggressive real-estate development ambitions. The hypothesis is that the integration of climatic data and regenerative design with modern cultural heritage classification frameworks can aid the preservation process, drive a more adaptive and inclusive approach to urban regeneration, and inform legislative integration of climate adaptation in conservation frameworks. To test this hypothesis, a multi-scalar case-study-based methodology is adopted using a combination of digital tools to assess and analyze the current and future impacts of climate change on two main case studies. First, the St. George Hotel & Bay, one of the first reinforced concrete recreational buildings in the city, was built during the French Mandate (1920–1946) and is vulnerable to sea-level rise, flooding, and demolition. Second, the Beirut City Center “The Egg”, a Brutalist structure built during Beirut’s modernist “golden era”, which is prone to structural deterioration and demolition. The main objective is to highlight 20th-century built heritage as part of Beirut’s spatial narrative worthy of conservation and rehabilitation by analyzing their capability to adapt to, mitigate, or benefit from future environmental risk. Ultimately, the study explores their potential to catalyze climate-resilient urban regeneration practices in the city. Results show that the integration of current and future forecast environmental analyses informed early preservation and intervention decision-making stages to position 20th-century modern built heritage as an asset to climate action in addition to being a socio-cultural and economic asset. 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

High-density cities require sustainable urban planning approaches that can screen recurrent surface heat while accounting for existing building-stock conditions. This study examines whether building-stock age composition, interpreted as a proxy for urban-renewal conditions and building-stock management needs, is associated with surface-heat persistence in Seoul, South Korea. Using 19 cloud-filtered summer Landsat Collection 2 Level-2 scenes from 2019 to 2025 and 485,473 building-age polygons, we construct a 250 m grid-cell evidence matrix. q75/q90 persistence is defined as the proportion of valid scenes in which a grid cell falls within the upper-tail scene-normalized LST distribution. Area-weighted mean building age is positively associated with q75 and q90 persistence; a p25-to-p75 increase in mean age corresponds to a 5.17 percentage-point increase in q75 persistence (95% CI: 4.54–5.81). The association is retained under spatial-filtering, spatial-lag-covariate, valid-pixel coverage-proxy, year-balanced, and threshold-universe checks. A planning-priority classification identifies 238 very-high-priority cells and 1166 high-priority cells for field review, roof/material surveys, vegetation/shading audits, and retrofit-feasibility assessment. The framework is a screening and prioritization tool, not evidence of causal redevelopment effects or direct human thermal exposure. Full article

Urban Building Energy Modelling (UBEM) is increasingly recognised as a key tool for bridging building-scale analysis and city-level planning, supporting the decarbonisation of urban areas. It enables the assessment of building performance and the exploration of retrofit strategies, policy scenarios, and renewable integration. Over the past decade, UBEM has continuously evolved, encompassing physics-based, data-driven, and hybrid approaches, often supported by archetype generation and GIS workflows. Recent research also integrates microclimate, mobility, and green infrastructure. While this enriches analysis outcomes, it also leads to fragmentation, limiting tools’ comparability, transparency, and practical use by policymakers. This perspective paper provides a critical interpretation of the recent evolution in UBEM and proposes a goal-oriented classification of modelling approaches based on their final objectives rather than on calculation methodology. The paper also identifies three key gaps: the lack of scalable and consistent data frameworks, the absence of a shared terminology, and the need for criteria to assess whether models are “fit for purpose.” Addressing these issues through improved data infrastructures, conceptual clarity, and reliability assessment can support convergence without limiting innovation, fostering more robust and decision-relevant UBEM applications to support the energy transition. Full article

As China shifts from expansion-oriented urbanization to stock-based renewal, cities need diagnostic tools that can identify built-environment deficiencies and translate them into spatially targeted renewal priorities. Building on China’s official urban physical examination system, this study develops a land-renewal-oriented diagnostic workflow across four spatial scales: housing, community, block, and urban area. The framework integrates top-down objective assessment of land, facilities, infrastructure, environment, and safety conditions with bottom-up resident satisfaction evaluation. It then converts composite health scores, problem concentration, and safety relevance into three renewal priority categories: Priority Level III (critical renewal), Priority Level II (general renewal), and Priority Level I (long-term optimization). Using Wu’an, a resource-dependent county-level city in Hebei Province, as a case study, the results show that Priority Level III problems are concentrated mainly at the housing scale, especially corridor safety hazards, while widespread pipeline aging and age-friendly retrofitting needs are classified as Priority Level II medium-term renewal issues. Community and block scales mainly show facility, governance, and functional mismatches. The contribution of this study is not the four-tier scale structure itself, which is derived from existing policy, but the operational translation of urban physical examination results into spatial diagnosis for land renewal, renewal priority classification, and action-plan formulation. The workflow offers a transferable methodological reference for county-level stock renewal, while local indicators, thresholds, and implementation pathways require contextual adaptation. Full article

Fine-scale identification of urban building functions is essential for understanding urban spatial structure, socioeconomic organization, and sustainable urban development. However, large-scale building function mapping remains constrained by reliance on proprietary data, insufficient representation of geographical context, and limited cross-city generalization. To address these challenges, this study proposes a multi-source geographical knowledge fusion framework for fine-scale building function classification using exclusively open-source data. In addition to conventional morphological, POI-based, and spectral features, the framework systematically integrates open-source geographical environmental features to characterize accessibility, infrastructure relationships, ecological surroundings, and environmental conditions at the building level. Beijing is selected as the training area, while Tianjin is used for independent cross-city validation. Ten representative models, including deep learning, ensemble learning, and traditional machine learning methods, are systematically evaluated for classifying six building function types. Results show that the systematic integration of geographical environmental features improves classification performance and interpretability. Deep learning and ensemble models outperform traditional methods, with CNN achieving the highest accuracy of 87.07% in Beijing and 69.83% in Tianjin. Feature contribution analysis further indicates that geographical environmental features play a dominant role in functional discrimination, while POI features provide important socio-semantic information. Overall, this study provides a reproducible open-data framework for urban building function mapping, supporting sustainable urban planning, land-use optimization, infrastructure allocation, and smart city governance. Full article

Monitoring the vertical progress of reinforced-concrete buildings supports construction management, urban analytics, and seismic exposure classification, yet camera-based floor counting faces two obstacles: public datasets depict almost exclusively completed structures, and the number of structurally finished floors is visually ambiguous while a building is still being erected. We reformulate building-height estimation as discrete floor-count classification from a single street-level facade image and assemble a 29,049-image multi-source corpus centered on the reinforced-concrete urban stock of Kazakhstan, including a 12-month, fixed-viewpoint sequence of 2255 frames that isolates invariance to construction stage, illumination, weather, and season. We formalize a reproducible annotation protocol for three recurring structural ambiguities—incomplete upper floors, rooftop superstructures, and open ground-level pilotis—and propose DINOv2-MSTS, a dual-branch architecture that aggregates multi-scale patch-token statistics from a frozen self-supervised backbone, trained with an Ordinal-Aware Annotation-Uncertainty (OAU) loss for which its Gaussian spread is learned rather than fixed. On the 5359-image Korter + Mendeley 21-category benchmark, the model attains 80% top-1 accuracy, 94% within $\pm 1$ floor accuracy, and 0.28-floor mean absolute error on this saturated 21-category task (a lower bound for buildings of 21 or more floors) using only 1.84 M trainable parameters, 165× fewer than a fully fine-tuned Vision Transformer, which it outperforms by eight accuracy points. On the separate 2255-frame IITU fixed-label robustness probe, it preserves the correct six-floor prediction in 91% of frames (0.09-floor MAE). The corpus, protocol, architecture, and loss together provide a reproducible benchmark for construction-stage building monitoring. Full article