Search Results (449)

Urban flood peak mitigation by green infrastructure (GI) is fundamentally a timing problem. Because GI storage is finite, interception occurs only within a brief active window; whether it reduces the outlet peak depends on GI placement in the network, routing lags, and rainfall timing. Here, we develop a timescale-based framework that links outlet peak reduction to the alignment among within-storm temporal structure, network response, and GI filling dynamics, providing a compact way to interpret when different network positions become most effective under a fixed GI design. Starting from a general convolution representation of runoff generation, interception, and routing, we show that peak reduction efficiency and location ranking can be organized by two nondimensional ratios—comparing storm duration and network response time to a characteristic GI filling time—plus simple descriptors of within-storm temporal structure. Under uniform rainfall, these ratios yield an interpretable regime diagram with analytical transition curves between downstream-, mid-network-, and upstream-optimal placement for a generic dispersive routing representation. Relaxing the uniform-rainfall assumption shows that within-storm variability can substantially reorganize these regimes because storm timing controls both how long GI storage remains available before it fills and which routed contributions overlap to form the outlet peak. Highly concentrated storms and storms with early internal peaks are especially likely to reorder the ranking of candidate locations relative to the uniform-rainfall baseline. Using 2351 observed hourly storm events evaluated across virtual catchments spanning fast to slow network responses, we quantify how often realistic event structure alters the optimal location and the regret associated with adopting a uniform design storm. The results motivate robustness-oriented placement strategies based on ensembles of plausible storm temporal structures, organized within the proposed timescale diagram rather than reliance on a single design hyetograph. Full article

Urbanization significantly reshapes urban form, affecting the spatial and quantitative dynamics of urban land use under carbon constraints. However, the role of macro-scale urban form in guiding low-carbon urban expansion remains underexplored. Our study introduces an integrated Cellular Automata (CA) model to simulate urban land use patterns with regard to the low-carbon goal, focusing on urban form optimization. The model employs a top-down strategy to adjust future urban land demand by balancing urban development needs with carbon emission (CE) reduction targets. The adjusted demand is then used to optimize urban form parameters (i.e., the inverse S-shaped function) to predict future urban land patterns and allocate land increments within concentric rings. Subsequently, a bottom-up strategy incorporating carbon sequestration (CS) conservation is applied to refine urban land conversion. The CA model integrates a maximum probability transformation rule to allocate urban land efficiently. We used the model to simulate urban land use patterns under four scenarios (i.e., Low-carbon Urban Development Scenario (L-UDS), Top-up Urban Development Scenario (T-UDS), Bottom-up Urban Development Scenario (B-UDS), and inverse S-shaped constraint Urban Development Scenario (S-UDS)) for the Changsha–Zhuzhou–Xiangtan (CZX) urban agglomeration in 2035. Results show that the proposed model effectively reconciles the conflict between rapid urbanization and urban carbon management strategies, as evidenced by a 31.25% reduction in carbon emissions in the L-UDS and T-UDS relative to the S-UDS and B-UDS. Furthermore, urban form constraints promote the development of compact and dense urban structures, advancing sustainable urban development goals. This study not only proposes a simulation model capable of effectively promoting compact urban development at the theoretical level, but its findings also offer actionable policy insights for China to address urban sprawl and actively advance low-carbon urban development. Full article

This paper investigates the application of Transit-Oriented Development (TOD) principles to the retrofitting of historic Gulf urban cores through a comparative analysis of Souq Waqif (Doha) and Qasr Al Hokm (Riyadh). The research employs field observation, thematic mapping, and qualitative diagnosis using the Integrated Modification Methodology (IMM) to assess compactness, intricacy, and connectivity within walkable station catchments. The findings indicate that Souq Waqif has a highly compact and intricate historic core with robust pedestrian activity, yet exhibits discontinuities at its periphery, such as car-dominated streets, fragmented green spaces, and weak connections between the metro station, parks, and adjacent blocks. In Qasr Al Hokm, the analysis affirms the value of its fine-grained historic fabric and civic landmarks, but also identifies deficiencies in shading, last-mile connectivity, and land-use balance surrounding the new metro station. Drawing on lessons from Souq Waqif, the paper proposes a TOD-oriented urban design framework for Qasr Al Hokm, emphasizing shaded pedestrian corridors, active ground floors, intermodal hubs, and heritage-compatible mixed-use intensification. This comparative approach demonstrates how TOD can foster more livable, accessible, and climate-responsive historic cores in Gulf cities, while maintaining respect for local identity and governance structures. Full article

Urban expansion and spatial restructuring significantly influence the urban thermal environment. This study investigates the central urban area of Jinan, developing a multi-dimensional spatial structure index system that integrates terrain, 2D/3D morphology, and layout based on multi-source data. Land surface temperature (LST) was derived from remote sensing imagery. Using road networks and triangulated irregular networks (TINs) generated from a digital elevation model (DEM), hybrid analysis units were created. Pearson correlation and bivariate global/local spatial autocorrelation analyses were applied to examine the mechanisms and spatial heterogeneity of how urban spatial structure affects LST. The results showed that (1) LST was strongly associated with urban spatial structure. Among the 12 significantly correlated indicators, building density showed the strongest positive correlation with LST (r = 0.5883), while DEM mean had the strongest negative correlation (r = −0.7444), indicating that compact built-up areas intensified heating, whereas terrain most strongly moderated surface temperature. (2) LST and indicator correlations varied with elevation. LST showed a negative correlation with the standard deviation of DEM, suggesting that greater terrain variability enhances cooling effects. This spatial variation in the dominant drivers of the thermal environment reflects a clear divergence of influencing factors across different elevational zones. The thermal environment exhibits a pronounced north–south split: cooling effects prevail in the south due to terrain, while warming effects dominate in the north due to building forms. (3) Bivariate spatial autocorrelation revealed clear spatial heterogeneity. High–high clustering of LST and spatial structure indicators in the northern plain denoted heat-aggregated zones. Low–low clustering in the topographically complex, sparsely built south formed cold-source zones, and transitional areas showed mixed high–low and low–high clustering. (4) Based on these findings, a zonal governance framework was advocated, prioritizing terrain assessment followed by spatial structure optimization. This promoted a shift from uniform to precise, zone-based thermal environment management, laying a scientific foundation for sustainable spatial planning. Full article

Uncontrolled urban sprawl represents a significant challenge for many countries worldwide. This article analyzes discussions sparked by increased land consumption driven by urban sprawl in Timișoara’s peri-urban area, Romania. In this context, the objective is to identify the processes of transformation and dysfunction of spaces under the effect of peri-urban expansion. Methodologically, geospatial and satellite data were utilized to assess the evolutionary trend of peri-urbanization. Secondly, an evaluation of land-use types at the level of peri-urban sub-neighborhoods was conducted. Furthermore, an analysis of the online reactions of the inhabitants of these spaces was conducted. The results demonstrate differentiated urban growth patterns in the spatial expansion dynamics of peri-urban spaces, which have emerged along major communication axes and in response to the configuration of available land and proximity to the urban core of each locality. Based on LCR/PGR indicators, these patterns can be further categorized into compact and expansive growth models. Furthermore, deficiencies in fundamental infrastructure pose a significant challenge for a considerable proportion of the local population. The study provides authorities and urban planners with reflective analyses on how to better manage peri-urban development. The results of the study support coherent, preventive, and sustainable urban development to avoid replicating the dysfunctions observed in the studied areas in other peri-urban areas. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

Urban expansion has intensified concerns regarding land use efficiency and sustainable urban development worldwide. Despite growing global application of Sustainable Development Goal (SDG) Indicator 11.3.1, comprehensive assessments within the United States (U.S.) remain limited. This study evaluates urban land use efficiency in the contiguous U.S. between 2000 and 2020 by examining the relationship between land consumption and population growth using the ‘Land Consumption Rate to Population Growth Rate’ (LCRPGR) framework. Urban land expansion and population change were derived from integrating impervious surface data with gridded population estimates, respectively, and the indicator was calculated for 2229 urban areas to evaluate temporal and regional patterns. Results show that urban land area increased by 23% over the study period, while population grew by 31%, indicating an overall shift toward denser urban development. The median LCRPGR declined from 0.84 during 2000–2010 to 0.63 during 2010–2020, reflecting improvements in land use efficiency, although notable regional disparities remain. Cluster analysis reveals distinct spatial growth patterns, with older metropolitan areas and western cities generally exhibiting more compact development. Findings demonstrate the applicability of SDG Indicator 11.3.1 for evaluating urban land use efficiency in a U.S. context and highlight the importance of coordinated spatial planning and policy measures to support sustainable urbanization. Full article

Accurate and real-time vehicle detection is a fundamental requirement for smart urban traffic monitoring, particularly in densely populated cities where heterogeneous traffic, frequent occlusion, and severe scale variation challenge lightweight vision systems deployed at the edge. To address these issues, this paper proposes BDNet, a lightweight YOLOv12-based vehicle detection framework designed to enhance feature preservation, contextual modeling, and multi-scale representation for intelligent transportation systems. BDNet integrates three complementary architectural components: (i) HyDASE, a hybrid detail-preserving downsampling module that mitigates information loss during resolution reduction; (ii) C3k2_MogaBlock, which strengthens long-range contextual interactions through multi-order gated aggregation; and (iii) an A2C2f_FRFN neck, which refines multi-scale features by suppressing redundancy and emphasizing discriminative responses. To support evaluation under realistic developing-region traffic conditions, we introduce the Bangladeshi Road Vehicle Dataset (BRVD), comprising 10,200 annotated images across 13 native vehicle categories captured under diverse urban scenarios, including daytime, nighttime, fog, and rain. On BRVD, BDNet achieves 85.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 67.3% ${m\mathcal{A}\mathcal{P}}_{50-95}$, outperforming YOLOv12n by +1.4 and +0.7 percentage points, respectively, while maintaining a compact footprint of 2.5 M parameters, 6.0 GFLOPs, and a real-time inference speed of 285.7 FPS. Cross-dataset evaluation on VisDrone-DET2019, using models trained exclusively on BRVD, further demonstrates improved generalization, achieving 31.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 17.9% ${m\mathcal{A}\mathcal{P}}_{50-95}$. These results indicate that BDNet provides an effective and resource-efficient vehicle detection solution for smart city–scale urban traffic monitoring. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article

The combined effects of urbanization and climate warming subject cold coastal cities to summer heatwaves and winter extreme cold, yet most studies emphasize built-environment modifications for summer overheating and lack evaluation methods and planning-oriented strategies to balance seasonal trade-offs. Using Dalian as a case study, we develop a seasonal net-benefit model that quantitatively characterizes and reconciles seasonally differentiated built-environment effects on land surface temperature (LST) and interprets urban heterogeneity within the Local Climate Zone (LCZ) framework. Summer LST is mainly governed by static factors such as greenspace configuration and topography, whereas winter LST is more sensitive to development intensity and locational factors, including building density and the Normalized Difference Built-up Index (NDBI). Coastal areas and mountainous green corridors are net-benefit zones performing well in both seasons, while dense industrial and compact low-rise areas account for ~80% of pronounced net-penalty zones. Compact mid- and high-rise neighborhoods show more favorable structural climatic conditions but with substantial retrofit potential (Retrofit Seasonal Net-Benefit Index (R-SNBI) markedly lower than Structural Seasonal Net-Benefit Index (S-SNBI) by ~3). Large low-rise problems mainly stem from an unfavorable structure rather than insufficient greenness, whereas industrial land has greater improvement potential via blue–green spaces. The framework supports refined climate adaptation, sustainability-oriented planning, and identifying urban renewal priority areas in cold-climate cities. Full article

Rapid urbanisation in coastal megacities intensifies coupled climate and water-related challenges, including heat stress, ventilation deficits, and increasing sensitivity to hydro-climatic extremes. Urban morphology plays a critical role in regulating these climate–water interactions by shaping airflow, surface heat exchange, and the spatial organisation of green–blue infrastructures. This study develops a Local Climate Zone (LCZ)-conditioned generative modelling framework based on a Conditional Pix2Pix Generative Adversarial Network, using paired LCZ classification maps and urban morphology data derived from six representative cities in the Guangdong–Hong Kong–Macao Greater Bay Area: Guangzhou, Shenzhen, Hong Kong, Macao, Zhuhai, and Dongguan. By integrating remote sensing–derived LCZ classifications with urban morphology data, the proposed framework learns spatial patterns associated with key morphology-related predictors, including building density and compactness, height-related structural intensity, open-space distribution, and the continuity of green–blue and ventilation corridors. The model demonstrates robust performance (SSIM = 0.74, R² = 0.81, PSNR = 15.3 dB) and strong cross-city transferability, accurately reproducing density transitions, ventilation corridors, and green–blue spatial structures relevant to coastal climate and water adaptation. The results highlight the potential of LCZ-informed generative modelling as a scalable decision-support tool for climate–water adaptive urban planning, enabling rapid exploration of morphology configurations that support heat mitigation, ventilation enhancement, and resilient coastal transformation. Full article

This paper focuses on whether smart-related built environments are associated with improved housing affordability for economically disadvantaged groups. Smart growth is a planning theme that aims to address the unintended negative consequences of urban sprawl through combining diverse dimensions across land-use diversity, housing diversity, accessibility, and compact development. Focusing on Clackamas County, Multnomah County, and Washington County within the Portland metropolitan area, the analysis uses census-tract-level data to assess both contemporaneous associations in 2013 and changes in affordability between 2013 and 2019. Overall, the findings suggest that smart-growth tools exhibit both potential and limitations with respect to housing affordability. Greater housing-type diversity and lower reliance on single-family residential land use are consistently associated with higher shares and subsequent increases in affordable housing units for low-income groups. In contrast, other smart-growth features—such as land-use mix and accessibility—show weaker or uneven relationships. These findings suggest that smart growth can contribute to expanding affordable housing supply primarily through housing-related components, while other dimensions of smart growth appear to play a limited role. The results underscore that housing-focused strategies play an important role in shaping affordability outcomes under smart growth. Full article

Against the backdrop of the “dual carbon” strategy, enhancing energy utilization efficiency and promoting low-carbon urban heating have become key directions for energy system transformation. Due to the compact structure, high heat transfer efficiency, and strong adaptability, herringbone plate heat exchangers have emerged as critical intermediate heat exchange equipment in long-distance heating systems. This paper reviews research on the heat transfer performance of herringbone plate heat exchangers, systematically examining fluid flow patterns within plate heat exchangers and the mechanisms influencing thermohydraulic performance under single-phase and two-phase flow conditions, along with recent advancements. First, factors affecting fluid flow within herringbone corrugated plates are introduced. Subsequently, recent experimental and numerical simulation advancements under single-phase and two-phase conditions are presented, along with corresponding performance correlation equations. In contrast, two-phase heat transfer mechanisms are more complex, with relatively insufficient research and a lack of universally applicable theoretical models and performance correlations. This paper argues that future efforts should focus on strengthening research into two-phase flow heat transfer mechanisms and developing more universal and predictive performance models to support the efficient application of plate heat exchangers in low-carbon heating and industrial energy conservation. Full article

Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, most approaches still rely on static fusion and lack explicit multi-scale alignment. As a result, they struggle to adaptively integrate heterogeneous inputs and suppress cross-modal interference, which constrains representation learning. To overcome these limitations, we propose BuildFunc-MoE, an adaptive multimodal Mixture-of-Experts (MoE) network built on an effective end-to-end Swin-UNet backbone. The model treats high-resolution remote sensing imagery as the primary input and integrates auxiliary geospatial data such as nighttime light imagery, DEM, and point-of-interest information. An Adaptive Multimodal Fusion Gate (AMMFG) first refines auxiliary features into informative fused representations, which are then combined with the primary modality and passed through multi-scale Swin-MoE blocks that extend standard Swin Transformer blocks with MoE routing. This enables fine-grained, dynamic fusion and alignment between primary and auxiliary modalities across feature scales. BuildFunc-MoE further introduces a Shared Task-Expert Module (STEM), which extends the MoE framework to share experts between the main BFI task and auxiliary tasks (road extraction, green space segmentation, and water body detection), enabling parameter-level transfer. This design enables complementary feature learning, where structural and contextual information jointly enhance the discrimination of building functions, thereby improving identification accuracy while maintaining model compactness. Experiments on the proposed Wuhan-BF multimodal dataset show that, under identical supervision, BuildFunc-MoE outperforms the strongest multimodal baseline by over 2% on average across metrics. Both PyTorch and LuoJiaNET implementations validate its effectiveness, while the latter achieves higher accuracy and faster inference through optimized computation. Overall, BuildFunc-MoE offers a scalable solution for fine-grained BFI with strong potential for urban planning and sustainable governance. Full article

This study presents an optimal design methodology for a piezoelectric-based bladeless wind turbine (BWT) that efficiently converts wind-induced vibration of a cantilever-mounted cylinder into electrical energy. A lumped-parameter model integrating structural dynamics, fluid-structure interaction, and piezoelectric energy conversion is introduced and simplified to derive key dimensionless design parameters and optimal conditions for maximizing power output. The optimal design criteria are as follows: tuning the resonance between the structural natural frequency and vortex shedding frequency; setting the dimensionless load resistance R* to unity; and minimizing ω_nR_LC_eq to a value smaller than unity. Numerical simulations and wind tunnel experiments validate the model, showing good agreement with less than 7% error in power prediction under resonance conditions and successfully predicting the coupled behavior of fluid, structure, and piezoelectric components. The proposed optimal design methodology facilitates the development of compact and efficient piezoelectric-based bladeless wind energy harvesting systems suitable for urban and space-constrained environments. Full article