Search Results (379)

A 700 m pilot-scale cast iron pipeline reactor was operated for 120 days to investigate corrosion-stage evolution under reclaimed-water conveyance conditions. Sampling points were arranged at 50, 250, 450, and 650 m, and water-quality monitoring, coupon weight-loss tests, scanning electron microscopy (SEM), and high-throughput 16S rRNA sequencing were combined to characterize corrosion-rate variation, corrosion-product morphology, and microbial community succession. During transport, NH₄⁺ generally decreased while NO₃⁻ increased, indicating nitrification-related nitrogen transformation under aerobic conditions; meanwhile, PO₄³⁻ declined and DOC fluctuated, reflecting coupled physicochemical and biological processes. SEM observations showed a transition from loose porous deposits to relatively compact layered corrosion products, followed by local deterioration and renewed porous structures in the later period. The corrosion rate followed an increase–decrease–re-increase pattern rather than a monotonic trend. Therefore, corrosion acceleration (CA = dc/dt) was introduced as an auxiliary diagnostic indicator to identify whether corrosion activity was increasing, decreasing, or temporarily stabilizing. Microbial community analysis showed stage-associated variation in biofilm and nitrogen-transformation-related taxa, supporting the interpretation that corrosion evolution was jointly affected by water-quality change, corrosion-product development, and microbial succession. Overall, the combined interpretation of corrosion rate, CA, water quality, SEM morphology, and microbial succession provides a more informative basis for diagnosing corrosion-stage transitions in reclaimed-water cast iron pipelines. From a sustainability perspective, this diagnostic framework can support long-term operation, maintenance planning, and risk monitoring of urban reclaimed-water distribution infrastructure, thereby improving pipeline durability, reducing leakage and maintenance risks, and enhancing the reliability of reclaimed-water reuse systems. Full article

Road network extraction and updating are crucial for urban development, map updating, and mobility applications. Existing trajectory-based methods often underutilize grid-level semantic information and neighborhood context, thereby limiting their robustness to noisy, heterogeneous, and cross-city trajectory conditions. This study proposes a supervised framework for trajectory-driven road network extraction by coupling intra-grid movement semantics with inter-grid neighborhood context. Multi-level features, including convex-hull shape descriptors, directional clustering, DTW-based (Dynamic Time Warping) heterogeneity, and neighborhood density differences, are used to train a Random Forest classifier for key-grid detection. The detected key grids are further processed through morphology-aware thinning and Kalman smoothing to generate a topology-preserving and vectorization-ready road skeleton. The model is trained on pedestrian trajectories from Shenzhen and directly transferred to vehicle trajectories in Wuhan and Changsha under a zero-shot setting. Experimental results show that the proposed method achieves longer correctly extracted road length and competitive length-based precision compared with raster-based reference methods, while feature-importance and ablation analyses confirm the complementary role of neighborhood context. The proposed pipeline is scalable, interpretable, and transferable, supporting trajectory-based road map updating and urban network analysis. Full article

This study aims to identify the defining characteristics of pocket parks and evaluate their ecological and socio-economic significance by analyzing their contribution to sustainable development, in alignment with the 17 United Nations Sustainable Development Goals (SDGs). This research highlights the benefits of green spaces and pocket parks in relation to the three core pillars of sustainability, mapping them directly onto specific SDG Targets and indicators. This framework informs the creation of a streamlined, early design indicators toolkit. The toolkit’s practical utility is then evaluated and validated through its application to four real-world case studies, where the performance of pocket parks is assessed regarding their contributions to urban sustainability. The selected case studies represent diverse morphological typologies and operational attributes. To embed sustainability benefits into the active planning process, their spatial design criteria were cross-examined to identify structural interconnections, which were subsequently translated into a parametric model. Each design parameter is analyzed with emphasis on the relationships among spatial elements rather than on their absolute metric values. The study develops a procedural design sequence that, when applied to any site boundary, generates the essential spatial characteristics defining a pocket park. The results demonstrate that this parametric approach establishes the adaptability and effectiveness of pocket parks as versatile urban green spaces, regardless of available plot size or geometric configuration. Full article

Traditional urban building energy modeling often overlooks the complexity of spatial configurations and mutual shading effects, thereby limiting its accuracy. This study proposes a novel, interpretable, data-driven framework based on heterogeneous graph neural networks (GNNs) to uncover and characterize the complex interrelationships between building morphology and urban topology. Using a parametric platform, this study generated a graph dataset of 285 residential blocks in Wuhan, structured as a dual-level graph: Building Zone Graphs (BZGs) and Building Layout Graphs (BLGs). Four GNN models were trained based on the dataset, and the evaluated results demonstrate that GraphTransformer outperforms GCN, GAT, and GraphSAGE in capturing long-range spatial relationships―particularly those arising from shading and solar access interactions. On a validation set, GraphTransformer achieved superior predictive accuracy, with R² scores exceeding 0.85 and 0.90 for cooling and heating energy predictions, respectively. After that, post hoc interpretability analysis by GNNExplainer identified three important morphology features influencing building energy consumption. Critically, the model found that shading relationships encoded as graph edges―especially those between southern and western façades―had statistically significant influence on building energy consumption. Finally, this work establishes an efficient, interpretable surrogate modeling framework for urban-scale energy analysis, delivering quantifiable, design-actionable insights to support sustainable urban development. Full article

Amid China’s rapid urbanization, many historic districts face complex challenges, including fragmented traditional fabrics, disordered spatial morphology, and discontinuous street networks. To tackle these issues, this study proposes a multimodal deep learning framework that combines Generative Adversarial Networks (GANs) and Diffusion Models, establishing an integrated generation-optimization workflow for the renewal of historic districts. The methodology begins by using Pix2PixHD to generate high-precision fabric layouts, followed by fine-tuning a Diffusion Model through Low-Rank Adaptation (LoRA) to achieve diversified morphological expansion. The candidate proposals are quantitatively evaluated using a ten-indicator evaluation matrix that covers both architectural fabric and street network dimensions. Afterwards, these proposals undergo iterative optimization with a multi-objective framework to enhance both urban fabric morphology and network performance. The framework was validated through an empirical study of the Yuehe Historic District in Jiaxing. The results indicate that the generated schemes closely align with the original urban fabric. Compared with the existing expanded area (EA), the weighted comprehensive fitness score of the optimized scheme group improved from 0.66 to 0.89 ± 0.02 (a 34.8% increase), with the standard deviation decreasing from 0.07 to 0.02, indicating significantly enhanced stability. Deep learning balances morphological authenticity, generative diversity, and performance in historic district preservation and renewal. Full article

Rapid urbanization and increasing landscape fragmentation pose significant threats to ecological connectivity, creating a need for integrative decision support approaches in sustainable spatial planning. This study presents a systematic review of ecological network optimization studies published between 2005 and 2025, following the PRISMA protocol. A total of 78 peer-reviewed studies were analyzed to identify methodological trends, recurring limitations, and research gaps in the assessment of structural and functional connectivity. Based on the gaps identified through the systematic review, this study proposes a conceptual Sustainable Spatial Decision Support System (S-SDSS) framework that integrates Morphological Spatial Pattern Analysis (MSPA), Multi-Criteria Evaluation (MCE/AHP), Minimum Cumulative Resistance (MCR), Least-Cost Path (LCP), and Gravity Modeling (GM) within a unified analytical structure. The review findings reveal a clear shift from single-method applications toward integrated multi-model approaches that better represent ecological processes and improve corridor prioritization. The proposed framework synthesizes the complementary strengths of these established methods to support evidence-based ecological network planning. The framework operates as a hybrid structure that combines a sequential analytical workflow with a unified typological classification system, generating Hybrid Ecological Typologies (T1–T5) as planning-oriented outputs that cannot be produced by any individual method alone. The proposed S-SDSS offers a transferable and policy-relevant conceptual basis for ecological network optimization, supporting green infrastructure planning, biodiversity conservation, and long-term landscape resilience across multiple spatial scales. Full article

Semantic segmentation of street-view imagery has become an important computational tool for urban morphological analysis and the evaluation of street spatial quality. However, existing methods still struggle in complex urban environments. Major challenges include large variations in building façade scales, degradation of boundary information, and severe class imbalance. These issues limit the ability of current models to capture structurally meaningful urban forms. To address these challenges, this study proposes a high-resolution street-view segmentation framework, termed HieraWaveSeg. The model aims not only to improve pixel-level segmentation accuracy but also to enhance the interpretability of urban morphology through structured representations of street space. Specifically, a Hiera Transformer backbone is employed to capture hierarchical spatial semantics. A Path Aggregation Network is further introduced to strengthen cross-scale feature interaction and improve structural consistency in complex scenes. In addition, a Wave Fusion module based on the Haar wavelet transform is incorporated to preserve fine-grained architectural details by enhancing high-frequency boundary and texture information during decoding. Unlike conventional segmentation approaches that primarily focus on object recognition, this study introduces a morphology-oriented semantic reconfiguration strategy. This strategy reorganizes original categories into functionally meaningful urban units. As a result, the segmentation outputs can be more directly linked to urban morphological indicators, such as façade continuity, spatial enclosure, and interface permeability, thereby improving interpretability in architectural and urban design contexts. To further address class imbalance, a composite loss function combining weighted cross-entropy and Dice loss is adopted, together with a median frequency balancing strategy. Experimental results on the CamVid and Cityscapes datasets demonstrate that the proposed method consistently outperforms several state-of-the-art baselines in both segmentation accuracy and structural preservation. Beyond quantitative improvements, the results indicate that the proposed framework generates more coherent and morphologically meaningful urban representations, supporting further quantitative analysis in urban morphology and architectural studies. Full article

Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual appearance features while neglecting the intrinsic geometric continuity and morphological characteristics associated with structural failures such as cracks and spalling. To address these challenges, this study proposes an enhanced defect detection framework termed GCA-YOLO for intelligent structural inspection. The proposed method integrates a Geometric Constraint Attention (GCA) module and a Residual Efficient Channel Attention (RECA) module to improve feature representation. Instead of explicit physical simulation, the GCA module embeds morphology-guided geometric priors into the attention mechanism using differentiable gradient and Laplacian operators. This enforces structural continuity perception and suppresses geometrically inconsistent responses caused by background noise. Furthermore, a geometry confidence gating mechanism adaptively modulates the contribution of morphological features, while the RECA module recalibrates channel-wise responses to enhance the representation of weak and low-contrast defects. To comprehensively evaluate the proposed method, experiments were conducted on three representative datasets, including a public crack dataset and two self-built datasets (one for peeling/detachment and one for crack defects). These datasets were collected from diverse civil infrastructure scenarios such as bridges, tunnels, and pavements under challenging conditions including low illumination, shadow occlusion, complex textures, and heterogeneous backgrounds. Compared with the baseline YOLOv11 model, the proposed GCA-YOLO framework improves mAP@0.5 by 2.2%, 2.5%, and 15.9% on the public crack dataset, the self-built peeling/detaching dataset, and the self-built crack dataset, respectively. Meanwhile, Recall is improved by 4.6%, 3.8%, and 33.1%, respectively, demonstrating the effectiveness of the proposed dual-attention framework in enhancing the completeness of defect localization and reducing missed detections. Despite these performance gains, the proposed framework maintains a lightweight architecture and does not introduce significant computational overhead. Experimental results demonstrate that the proposed framework achieves strong robustness, stable generalization capability, and favorable detection efficiency across different defect categories and engineering scenarios, demonstrating promising potential for intelligent infrastructure inspection, urban safety monitoring, and practical engineering deployment. Full article

Building change detection from bi-temporal remote-sensing imagery underpins urban planning, infrastructure monitoring, and disaster assessment. Existing deep-learning methods achieve high accuracy but rely on large parameter counts, while pixel-level supervision provides limited boundary guidance. We propose POCA-lite, a lightweight encoder–decoder with an inference-coupled geometry branch: three geometric prediction heads—distance transform, boundary, and center heatmap—whose outputs are fused back into the decoder via a feedback pathway active at both training and inference. On the LEVIR-CD benchmark under a unified retraining protocol, multi-seed evaluation shows that POCA-lite matches SNUNet in mean F1 while using 47% fewer parameters and 53% fewer FLOPs. Boundary F1 improves by 9.22 pp over the no-geometry baseline. Decomposition ablations reveal two complementary improvement sources: geometric supervision alone recovers 85% of the total gain, while the feedback fusion pathway recovers 92%; their combination achieves the full result. Geometry-aware targets outperform a generic multitask control. Cross-architecture transfer to SNUNet yields +1.06 pp F1. However, cross-dataset evaluation on WHU-CD shows that the method underperforms SNUNet on dense urban morphology, and zero-shot cross-dataset transfer is not established. These results indicate that inference-coupled geometric supervision is effective for lightweight, boundary-sensitive change detection on domains with well-separated building morphology, but its applicability is scope-bounded. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article

Coastal urbanization is increasingly constrained by legacy land-use patterns and escalating climate risks, yet long-term morphological trajectories remain poorly quantified due to the absence of multispectral data in pre-satellite archives. This study introduces a scalable deep learning pipeline that bridges a century-scale domain gap through an attention-enhanced Pix2Pix colorization stage and a few-shot U-Net++ segmentation stage, enabling automated reconstruction of urban expansion from panchromatic historical aerial imagery (1920–1971) and digital aerial photographs (1997) to contemporary very-high-resolution satellite data (2024) in Les Sables-d’Olonne, France. The novelty of the approach lies in coupling generative colorization with epoch-specific fine-tuning to overcome radiometric and annotation bottlenecks that have historically prevented quantitative urban reconstruction from pre-satellite archives. The colorization stage achieved high spectral fidelity (PSNR 35.21 dB, SSIM 0.9762), and segmentation performed strongly on modern imagery (mIoU 0.9789). While the segmentation model performed strongly on modern imagery, direct transfer to historical data exhibited substantial domain shift due to radiometric discrepancies. Few-shot adaptation on year-specific calibration sets recovered reliable building footprints (mIoU 0.53–0.65) across the full timeline. Multi-scalar analysis of the reconstructed footprints revealed constrained anisotropic expansion: early saturation of the coastal historic core, followed by rapid inland peri-urbanization post-1971 driven by geographic barriers. This spatiotemporal shift has entrenched spatial lock-in, placing recent development in retro-littoral zones that are vulnerable to submersion and characterized by severe vegetation loss. The framework unlocks previously inaccessible historical archives for quantitative urban monitoring, providing critical insights into legacy effects of unconstrained growth and informing resilient coastal planning under climate change. Full article

Against the backdrop of rapid global urbanization, peri-urban villages universally face the dual dilemmas of landscape homogenization and the imbalance between heritage preservation and functional renewal. As a typical representative, the “Shanghai-style Jiangnan” villages feature an open water–land chessboard pattern and linear water-house parallel organization, which are distinctly different from the closed and introverted texture of traditional Suzhou-Hangzhou water towns. Such villages urgently need to balance the continuation of the original spatial fabric and the adaptation of modern functions. Existing studies on rural landscapes mostly focus on the static vertical identification of single elements, lacking a systematic quantitative analysis of the horizontal topological relationships among multiple elements, making it difficult to accurately define the spatial boundaries between preservation and renewal. This study takes Xinyuan Village in Jinshan District, Shanghai, as an empirical subject to construct a model for the vertical gene decoding of the “Point-Line-Network” and horizontal topology coupling of “Surface Gene.” By introducing a landscape sensitivity assessment combined with the Entropy Weight Method (EWM) and GIS (Geographic Information System) spatial Kernel Density Estimation (KDE), a quantifiable landscape control heat map is generated. The study identifies the nested original fabric structure of the “house-water-field-forest-road” and the spatial landscape differentiation characteristics in Xinyuan Village and delineates three-tier differentiated zoning controls through dual-verified heat maps. Validated based on Xinyuan Village, this method effectively resolves the conflict between rural preservation and renewal and realizes the transformation from static museum-style preservation to refined adaptive zoning. It provides specific practical strategies for the renewal of “Shanghai-style Jiangnan” villages and offers a quantitative morphological reference for enhancing the spatial resilience and living heritage of peri-urban villages, while its cross-regional transferability needs further verification. Full article

Urban renewal in China increasingly focuses on extensive residential historic areas embedded within cities. Recent practice emphasizes refined and progressive renewal strategies, which require multi-dimensional evaluations of micro-scale spatial elements and the formulation of differentiated renewal pathways. The progressive renewal also requires repeated evaluation and adjustment. However, conventional evaluation and decision-making largely rely on manual judgment, which can be subjective and inefficient when dealing with complex information. To address these limitations, this study proposes a digital methodological workflow integrating multi-objective analysis with design decision-making. The workflow targets spatial design issues at three hierarchical levels—streets, plots, and buildings—and is implemented through a multi-module platform. The platform consists of an analytical evaluation module and a design decision module. The analytical module provides quantitative assessments across morphological and non-morphological dimensions, while the design decision module combines analytical results with expected parameters to generate optimization suggestions for spatial structures and identify renewal pathways for spatial elements. Tested in the conservation and renewal planning of Hehuatang in Nanjing, the platform demonstrates the ability to efficiently compare spatial structure schemes and rapidly determine renewal pathways, improving the scientific rigor and efficiency of renewal planning. Full article

Forecasting short-term heavy precipitation is crucial for the early warning of disasters such as flash floods, landslides, and urban flooding. However, under complex topographic conditions, traditional numerical forecasts still fall short in capturing high-resolution heavy precipitation events, and conventional radar extrapolation methods struggle to accurately characterize the nonlinear evolution of weather systems during advection, deformation, and intensity adjustment processes. To address the challenge of short-term heavy rainfall forecasting in high-altitude, complex terrain, this paper proposes Nowcast with Flow-Net (Nwf-Net), a short-term precipitation forecasting framework that integrates deep learning with multi-source meteorological data. This framework consists of a Morphological Evolution Track Module (MET) and a Rainfall Intensity Correction Module (RIC) connected in series: the former combines upper-air wind fields with traditional optical flow algorithms to jointly characterize the displacement of and morphological changes in radar echoes; the latter utilizes a deep recurrent neural network to correct the intensity of forecast results, thereby enhancing the model’s ability to characterize the evolution of strong convective echoes. Experiments in the Alpine region demonstrate that Nwf-Net achieves CSI, HSS, and F1 scores of 0.392, 0.506, and 0.546, respectively, at 32 dBz. These results outperform those of traditional numerical models and some mainstream models, indicating that Nwf-Net can accurately capture multiscale severe convective information and consistently generate precise forecasts. Full article

Reliable and timely estimation of air pollution exposure at high spatial and temporal resolution remains challenging in complex urban environments, where pollutant concentrations vary due to traffic emissions, urban morphology, and meteorological conditions. This study presents a physics-informed machine learning framework for near-real-time estimation of NO₂ concentrations at fine spatial scales. The approach combines a limited set of steady-state computational fluid dynamics (CFD) simulations with operational meteorological and air-quality data. CFD simulations under specific wind directions are first used to characterize site-specific dispersion patterns. These outputs are then scaled using hourly meteorological observations to generate physics-based concentration descriptors. A machine learning predictor, implemented using Random Forest and Extreme Gradient Boosting, is trained to refine these estimates by incorporating additional environmental and observational features. The method is applied to a 1 km × 1 km urban district in Antwerp, Belgium, within the FAIRMODE intercomparison framework. Validation against measurements from 105 passive samples collected over one month shows substantial improvement compared to standalone dispersion modeling, with coefficients of determination up to R² = 0.965 and reduced bias across locations. These findings demonstrate that integrating physical modeling with machine learning enables accurate and computationally efficient high-resolution exposure assessment in urban settings. Full article