Search Results (1,158)

As China’s urbanization transitions from incremental expansion to stock-based renewal, industrial land redevelopment has become a key pathway for promoting high-quality urban development. However, existing studies mostly assess redevelopment potential from a single dimension and lack a systematic framework integrating ecological function (E), spatial structure (S), economic conditions (C), and building foundations (B). Taking the built-up area of Xi’an as a case study, this study adopts a functional–structural optimization perspective and constructs a four-dimensional ESCB assessment framework based on 13 indicators covering ecological function, spatial structure, economic conditions, and building foundations. GIS-based spatial quantification, MiniBatchKMeans clustering, and the XGBoost algorithm were employed to identify the redevelopment potential of industrial land, while SHAP analysis was used to interpret indicator contributions and determine the core influencing factors. The results show that industrial land in the study area can be classified into four types: vitality–density dominant, transport–scale coordinated, scale–facility lagging, and topography–vegetation sensitive, with significant differences in spatial distribution and indicator characteristics. The interpretable machine learning model further identifies road network density, block-level economic vitality, and land-use suitability as the three principal drivers of redevelopment potential, among which road network density plays the most critical role. By integrating clustering analysis with interpretable machine learning, the ESCB framework effectively reveals the synergies and trade-offs among multidimensional indicators and provides differentiated and precise support for industrial land redevelopment strategies. Full article

Accurate estimation of residential building energy consumption and associated CO₂ emissions is essential for refined urban carbon management. This study develops a hybrid framework that integrates physics-based simulation and machine learning to estimate residential building energy use and energy-related CO₂ emissions in Shenzhen in 2020. Representative building archetypes were first simulated and then used to train machine-learning models for large-scale applications. Building-level energy estimates were further combined with a bottom-up inventory to generate high-spatiotemporal-resolution maps of residential CO₂ emissions. The results show that: (1) the selected model achieved good accuracy and temporal robustness, with strong agreement between estimated and reference energy use at daily, monthly, and annual scales; (2) residential energy use was primarily driven by meteorological conditions, especially daily mean temperature and the duration of high-temperature conditions, and exhibited clear weekly and seasonal patterns, with higher values on weekends and in summer; (3) residential CO₂ emissions in Shenzhen reflected the combined effects of scale and intensity, with Longgang and Bao’an contributing the largest total emissions, Self-built residential buildings contributing the largest aggregate emissions, and Old residential buildings showing the highest average emissions per building; (4) emissions were highly concentrated in a small number of high-emission buildings, which were more frequently distributed along road-adjacent block perimeters. Overall, the proposed framework improves the fine-scale characterization of residential building CO₂ emissions and provides a useful basis for hotspot identification and targeted mitigation. Full article

Boosting urban vitality (UV) in residential living spaces has become a core component of advancing the people-centered urbanization strategy. However, previous research has mainly focused on exploring UV at the scales of streets, blocks, and grids, with few nonlinear explorations conducted across different temporal dimensions at the scale of residents’ daily life. Therefore, this article adopts the XGBoost-SHAP model to explore the nonlinear and interaction effects of a built environment (BE) on UV across multi-scale community life circles (CLC), distinguishing between daytime and nighttime on weekdays and weekends. The results indicate that UV on weekends is higher than on weekdays, except for 5 min CLC (5MCLC). UV is highest in 10 min CLC (10MCLC) and lowest in 5MCLC. The mean building height (MBH) and Normalized Difference Vegetation Index (NDVI) have always been the most important indicators affecting UV. Unlike previous studies, the green view index (GVI) and sky view factor (SVF) are negatively associated with UV. The nonlinear relationship between BE and UV on weekdays exhibits greater regularity. The effects of other BE indicators on UV exhibits spatiotemporal heterogeneity, with the relative influence changes in commercial accessibility (CA), distance to metro (DSM) and distance to bus (DSB) being the most significant. The nonlinear and threshold effects of BE on UV show significant changes, except for GVI, SVF, and NDVI, at different times and scales. The threshold for cultural and leisure accessibility (CLA) is higher on weekdays than on weekends, whereas that for DSM is higher on weekends than on weekdays. The interaction effects between the building density (BD) and MBH, park and square accessibility (PSA), and DSM is significant at different scales. This study will provide a scientific basis for optimizing BE and differentiated planning of CLC, which further contributes to enhancing UV and promoting urban sustainable development. Full article

Urban morphology, encompassing both horizontal landscape patterns and three-dimensional architectural structures, plays a pivotal role in modulating urban heat distribution. However, conventional models often fail to capture the intricate spatial nonstationarity and nonlinear coupling of these drivers at the block scale. Recognizing that land surface temperature (LST) exhibits distinct diurnal and nocturnal thermal cycles, this study explicitly incorporates spatial heterogeneity analysis to systematically evaluate the relative and local contributions, marginal effects, and interaction mechanisms of multidimensional urban morphology on diurnal LST variations. To achieve this objective, geographically weighted extreme gradient boosting and SHapley Additive exPlanations were employed to decipher these complex driving mechanisms from a morphological perspective. The results indicate the following: (1) Built environment variables predominate the spatial heterogeneity of LST in Xi’an, China, with their governing mechanisms shifting diurnally—characterized by a midday NDVI-induced evapotranspiration cooling effect and an atmospheric back-radiation warming effect associated with PM2.5 during the night and early morning. (2) The driving mechanisms exhibit pronounced spatial nonstationarity; while the northeastern and northern sectors are primarily influenced by the synergistic interaction between surface albedo and PM2.5, the central-western and southern regions are governed by population density and 3D architectural morphology. (3) Significant nonlinear interaction thresholds and non-monotonic response mechanisms were identified across the variables. By resolving localized thermal responses through the lens of spatial heterogeneity, this research provides a robust scientific framework for precision urban planning and the mitigation of the urban heat island effect. Full article

Nanoparticles (interchangeably called ultrafine particles) constitute one of the growing risks encountered in everyday life. Both short- and long-term exposure to them, as well as to particulate matter in general, may pose serious health risks. In this study, we focus on monitoring of particle concentration in urban air for 200 days, with special attention to nanoparticles. The overall data coverage exceeded 80%, reaching over 97% in three selected months. Measurements were carried out at 25.5 m height in southern Warsaw, in close vicinity to residential blocks with apartments also at the same level. Data were collected from January to first half of August 2025 using a Grimm MINI-WRAS portable wide-range aerosol spectrometer and a thermo-hygro-barometer. Over the 8-month period, significant variations between months and days in both nanoparticle and all particulate matter concentrations were observed. Winter months were almost four times more polluted with particles (both nanoparticles and those above 100 nm) than spring and summer periods. Although nanoparticle concentration in colder months was higher, the percentage of nanoparticles was lower. An important aspect of these investigations was comparing the obtained results with publicly available air pollution data from urban air quality monitoring stations, which represent ground-level measurements. At rooftop altitude, PM_2.5/PM₁₀ ratios were significantly higher than those measured at ground level. Full article

High-resolution remote sensing image change detection holds significant application value in the fields of urban planning, disaster assessment, and others. However, it faces the dual challenge of pseudo-change interference and loss of detailed information. To address these issues, a frequency-domain-aware Siamese detail recovery network (FSD-Net) is designed in this paper. Firstly, from the perspective of frequency domain analysis, a theory on the dual roles of frequency domain components is introduced to reveal the robustness of low-frequency components to pseudo-changes and the dual semantic noise attributes of high-frequency components. Based on this theory, a frequency-aware context-guided difference (FCGD) module is designed. By explicitly decoupling the difference features into low-frequency global components and high-frequency residual components, it utilizes the prior low-frequency scene as a semantic gate to adaptively modulate the high-frequency differences, which effectively suppress pseudo-change interference. Subsequently, a detail recovery block (DRB), based on sub-pixel convolution, is constructed. This achieves unbiased spatial rearrangement through the semantic redundancy of channel dimensions, which avoids the checkerboard artifacts of traditional upsampling, and by employing a progressive multi-stage upsampling strategy to integrate shallow detail features from the encoder. The experimental results on the three public datasets of LEVIR-CD, WHU-CD, and CDD-CD demonstrate that the FSD-Net outperforms current mainstream methods (e.g., ChangeFormer, BAN, and so on) in core metrics such as F1 score and IoU, with a particularly significant improvement in recall. The ablation experiments validate the effectiveness and complementarity of the FCGD and DRB. Parameter sensitivity analysis indicates that the auxiliary loss weight λ is dataset dependent, with λ = 0.1 serving as a robust default choice. This study provides an efficient and reliable solution for change detection in high-resolution remote sensing imagery. Full article

In the context of climate change and the challenge of strengthening urban biodiversity, this paper examines the potential of speculative drawing as a methodological tool for developing biodiversity scenarios of the cityscape within the framework of the New European Bauhaus initiative. The research is based on the initiative’s core values of beautiful, sustainable, and together, and is conducted using a drawing-based methodology grounded in inductive reasoning across three spatial scales in Block 30, which is part of the spatial cultural-historical unit of the Central Zone of New Belgrade. The potentials for biodiversity development are explored at the scale of the apartment, the facade, and the open space of the block. By examining the interactions between the indoor and open spaces of mass housing, ecological potentials emerge. The experimental process demonstrates that drawing can function as a methodological tool that reveals opportunities for community engagement through drawing practices. The proposed layering of drawings offers interpretations of cityscape transformation at each of the three scales. Through speculative scenarios, the drawings provide a methodological tool to co-create biodiversity interventions in mass housing as a sensitive architectural layer within the design process, fostering a new understanding of the relationship between nature and the cityscape. Full article

Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e.g., drone-embedded chips, satellite on-board processors) has become an urgent challenge—existing deep learning methods, despite high accuracy, are hindered by massive parameters and computational costs that preclude deployment on resource-constrained embedded hardware. To address this, we focus on lightweight (i.e., low parameter count and low computational cost) RSCD network design, targeting three critical bottlenecks: blurred boundaries of changed regions, missed detection of small objects, and insufficient computational efficiency. We propose ATCFNet (Adjacent-Temporal Cross Fusion Network), featuring a three-step progressive feature optimization strategy: (1) the Adjacent Feature Aggregation Module (AFAM) enhances shallow geometric details via lateral three-stage fusion to compensate for lightweight backbones; (2) the Temporal Attention Cross Module (TACM) integrates cross-level feature propagation and Convolutional Block Attention Module (CBAM) for collaborative optimization of high-level semantics and low-level details; and (3) the Efficient Guidance Module (EGM) establishes long-range dependencies using shared change priors and lightweight self-attention to suppress internal voids in changed regions. Experiments on three public datasets (LEVIR-CD, HRCUS, SYSU-ChangeDet) demonstrate that ATCFNet achieves state-of-the-art accuracy with merely 3.71 million (M) parameters and 3.0 billion (G) floating-point operations (FLOPs)—F1-scores of 91.46%, 77.05%, and 83.53%, significantly outperforming 18 existing methods in most indicators. Notably, it excels in edge integrity (avoiding jagged blurring at change boundaries) and small-target detection in high-resolution urban scenes. This study provides an efficient and reliable lightweight solution for edge computing scenarios such as real-time drone inspection and satellite on-board intelligent processing. Full article

Left-turn lanes are critical elements of urban intersections. Accurate and efficient lane detection is essential for the safe navigation of autonomous vehicles. To address the limitations of existing semantic segmentation algorithms—specifically, inadequate detection accuracy, high computational cost, and vulnerability to environmental disturbances—we propose a lightweight deep convolutional neural network named EA-UNet. First, we replace the standard U-Net encoder with EfficientNet-B0 to enhance feature extraction efficiency. Second, we introduce a novel contextual coordination module, termed MP-ASPP, which integrates a Convolutional Block Attention Module (CBAM) to further refine attention mechanisms. Finally, a comprehensive real-world dataset was constructed by collecting videos and images of left-turn waiting areas during real-vehicle testing. Experimental results demonstrate that EA-UNet significantly outperforms the baseline U-Net and other state-of-the-art models, achieving accurate and efficient segmentation of left-turn lanes even in complex scenes. Full article

Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article

Urbanization threatens amphibians through habitat loss and fragmentation. The critically endangered Hynobius yangi, endemic to Korea, faces severe habitat destruction from urban development. No previous study has simultaneously assessed physicochemical habitat quality and larval feeding ecology across restored and alternative wetlands for this species using fecal DNA metabarcoding. We compared 25 H. yangi spawning sites in Sasong New Town through long-term monitoring (April 2021–September 2024; 364 surveys) and fecal DNA metabarcoding (18S V9, COI313, and blocking primers) from 60 larvae. Egg sac abundance showed negative associations with habitat area (r = −0.21), pH (r = −0.23), and conductivity (r = −0.21); however, none retained significance after Bonferroni correction, and each explained only 4–5% of variance, indicating exploratory associations. Associated conditions included area 115.5 ± 16.2 m² (mean ± SE), circularity 44.2 ± 2.4%, pH 7.55 ± 0.10, and conductivity 53.0 ± 2.7 μS/cm. Dietary analysis identified 17 prey taxa. Larvae in alternative areas showed generalist feeding favoring Perlidae and Tubificidae, while restored-area larvae showed specialist patterns dominated by Chironomidae, Nematocera, and Psychodidae. Both habitat types supported H. yangi populations. These preliminary findings suggest that appropriately designed alternative areas may complement traditional restoration, pending multi-site validation. Full article

Seventy-one percent of the Earth’s surface is covered by water, with groundwater being one of the most important natural resources globally. In Purba Medinipur, the population growth rate has surged to ~0.75% per annum, outpacing that of West Bengal, due to agricultural and industrial development. Urbanization has led to an increase in the built-up area by 139.10% per annum, which has reduced the percolation of water into the groundwater table. Currently, 72% blocks are affected by salinity. Groundwater quality parameters such as pH, total dissolved solids (TDS), turbidity, iron, manganese, total hardness, and chloride were assessed over three seasons—pre-monsoon, monsoon, and post-monsoon—using 326 data points from 2015 to 2022. Turbidity and iron are the primary concerns for groundwater quality, contributing to pollution. Other parameters, including TDS and total hardness, were approaching acceptable limits across all seasons. Since 2021, turbidity has exceeded permissible limits during the pre-monsoon season, resulting from the dissolved minerals and seawater intrusion. The arithmetic weighted groundwater quality index has shown an increasing magnitude over time, indicating a decline in drinking water quality by 2030. The pre-monsoon season exhibits the most severely affected groundwater quality. Principal component analysis indicated that TDS and chloride are the major contaminants during the pre-monsoon, confirming seawater intrusion. In other seasons, metals like iron, TDSs, and manganese are significant contaminants. The hydraulic barriers, subsurface dams, and hybrid treatment can be adopted in the study area to abate the increasing groundwater quality concentration both on a yearly and seasonal basis. Full article

Recent studies suggest that prolonged participation in formal music programs may be associated with the cultivation of resilience and locus of control (LoC) in music students. Brazilian musicians, who were attending or had attended community-based music programs, and a group of matched, untrained individuals from disadvantaged, urban communities completed the Connor–Davidson Scale of Resilience (RISC), the Craig Locus of Control Scale, and the ABEP 2022—Brazilian Criteria of Economic Classification questionnaire. Results suggested that while musical participation alone was not associated with resilience and LoC scores (model 1), a conditional restriction of the same model (model 2) showed a significant interaction between musical participation, age, and RISC and LoC scores, after controlling for SES. Among musicians, higher age was associated with higher resilience scores and internal LoC. Findings from this exploratory study are discussed in light of the multifaceted nature of community-based music programs, the building blocks of resilience and LoC. We also comment on the potential links between resilience and LoC in relation to musical participation and well-being. Limitations of this study are discussed alongside implications for future research. Full article

Remote sensing semantic segmentation is fundamental to applications such as land-cover mapping, urban analysis, and environmental monitoring. However, remote sensing scenes often exhibit pronounced scale variation, fragmented regions, dense small objects, and complex boundary transitions, making fine-grained prediction particularly challenging. Transformer-based architectures such as SegFormer have demonstrated a strong capability in modeling long-range context through hierarchical encoding, yet their lightweight decoders mainly rely on linear projection and feature fusion, providing limited capacity for local refinement after multi-scale aggregation. This limitation may reduce spatial precision in boundary-sensitive and small-object-rich regions. To address this issue, we propose the Post-fusion Enhanced Block (PFEB), a lightweight decoder-side refinement module inserted after multi-scale feature fusion and before pixel-wise classification. PFEB combines channel expansion, depthwise and pointwise convolutions, efficient channel attention (ECA), and residual learning to enhance local semantic refinement while largely preserving computational efficiency. Built upon SegFormer, the proposed method was evaluated on two widely used remote sensing benchmarks, i.e., LoveDA and ISPRS Vaihingen, under both Mix Transformer-B0 (MiT-B0) and Mix Transformer-B2 (MiT-B2) backbones. Experimental results show that PFEB consistently improves the SegFormer baseline across datasets and model scales. Under MiT-B2 backbone, our method achieves 53.82 ± 0.31 mean intersection over union (mIoU) on LoveDA and 74.84 ± 0.41 mIoU on ISPRS Vaihingen. Boundary- and size-aware evaluations further indicate that the gains are mainly reflected in improved semantic correctness near boundaries and in the recoverability of small objects. With only modest additional cost (approximately +0.53 M parameters and +8.7 G floating point operations (FLOPs)), PFEB provides a favorable accuracy–efficiency trade-off. These results suggest that PFEB is an effective and lightweight post-fusion refinement module for improving fine-grained remote sensing semantic segmentation. Full article

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control. Full article