Search Results (425)

Early wildfire smoke detection is critical for preventing small ignitions from escalating into large-scale fires, yet early-stage smoke plumes are often faint, low-contrast, and spatially small. When full-resolution frames are resized to satisfy fixed-input detector architectures and enable efficient batched GPU inference, these subtle cues are further diminished, leading to missed detections and unreliable scores near deployment thresholds. Existing remedies such as multi-scale inference, slicing/tiling, or super-resolution could improve sensitivity, but typically incur substantial overhead from multiple forward passes or added network components, limiting real-time use on resource-constrained platforms. To mitigate these challenges, we propose a composite multi-resolution detection framework that improves sensitivity to small smoke regions while maintaining single-pass inference. Motivated by the fact that most operational wildfire monitoring systems rely on Unmanned Aerial Vehicle (UAV) platforms and mountain-top Closed-Circuit Television (CCTV) systems surveillance, their wide-field imagery typically contains a large sky region above the horizon where early smoke is most likely to first become visible. Accordingly, crop placement is guided by a skyline prior that prioritizes this high-probability sky band while retaining the remaining scene for global context. A dynamic compositing stage stacks a global view with a high-resolution, sky-aligned band into a standard square detector input, preserving context with minimal added cost. Detections from the two views are reconciled via coordinate restoration and non-maximum suppression. For deployment, a lightweight second-stage classifier selectively re-evaluates low-confidence detections to stabilize decisions near a fixed operating threshold without retraining the detector. Compared to the baseline detector, our approach improves detection performance on the Early Smoke dataset, achieving gains of +4.6 percentage points in AP @0.5:0.95, +3.4 percentage points in AP @0.5, +2.9 percentage points in precision, +5.3 percentage points in recall, and +4.3 percentage points in F1-score. Full article

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting. Full article

Perception in trellised orchards is often challenged by dense canopy occlusion and overhead plastic coverings, which cause pronounced variations in sky visibility at row terminals. Accurately recognizing row terminals, including both row head and row tail positions, is therefore essential for understanding orchard row structures. This study presents SkySeg-Net, a sky segmentation-based framework for row-terminal recognition in trellised orchards. SkySeg-Net is built on an enhanced multi-scale U-Net architecture and employs ResNeSt residual split-attention blocks as the backbone. To improve feature discrimination under complex illumination and occlusion conditions, the Convolutional Block Attention Module (CBAM) is integrated into the downsampling path, while a Pyramid Pooling Module (PPM) is introduced during upsampling to strengthen multi-scale contextual representation. Sky regions are segmented from both front-view and rear-view camera images, and a hierarchical threshold-based pixel-sum analysis is applied to infer row-terminal locations based on sky-region distribution patterns. To support a comprehensive evaluation, a dedicated trellised vineyard dataset was constructed, featuring front-view and rear-view images and covering three representative grapevine growth stages (BBCH 69–71, 73–77, and 79–89). Experimental results show that SkySeg-Net achieves an mIoU of 91.21% and an mPA of 94.82% for sky segmentation, with a row-terminal recognition accuracy exceeding 98.17% across all growth stages. These results demonstrate that SkySeg-Net provides a robust and reliable visual perception approach for row-terminal recognition in trellised orchard environments. Full article

This paper aims to establish the Neurobiophilia Index, a quantitative tool to assess environmental enrichment at the architectural scale based on neurobiological evidence. Using a literature review followed by an expert opinion workshop and deliberations, 10 items were identified: sky visibility, daylight, light/dark cycles, sound, odours, indoor plants, window green views, temperature, air quality, and materials. The index provides a symmetrical scoring system for each item based on the effect of its parameters on the brain: enrichment (+1), neutral (0), and harm (−1), facilitating assessment of architectural spaces and buildings to understand implications for neuroplasticity, with cognitive, mental health, stress recovery, and resilience outcomes. It is a useful tool for future research and provides a pathway toward advancing green building rating systems from sustainability to neurosustainability. Full article

Cycling accessibility is a key indicator of urban resource equity and built environment performance. However, its relationship with equity, the importance of built environment factors, and nonlinear effects across facility types remain underexplored. This study combines the Gini coefficient with a generalized additive model (GAM) to examine the nonlinear accessibility–equity relationship and uses Light Gradient Boosting Machine (LightGBM) with SHapley Additive exPlanations (SHAP) to assess the importance and nonlinear effects of built environment variables. Potential effects of vegetation (Normalized Difference Vegetation Index, NDVI) and precipitation are also explored. Results show that both accessibility and equity peak in urban cores and decline toward peripheral areas. Facility types exhibit distinct patterns: accommodation services have high accessibility but low equity, life services require higher accessibility to achieve equity, and public services show an N-shaped relationship. SHAP analyses indicate that traffic signals, subway station accessibility, and street enclosure ratio are associated with higher cycling accessibility, whereas a higher sky view index is associated with lower accessibility. Observed moderating effects of vegetation and precipitation provide further insights into potential interactions. These findings offer guidance for improving cycling infrastructure and promoting more equitable and sustainable urban accessibility. Full article

Rapid urbanization and spatial constraints in high-density residential areas pose significant challenges to public health and well-being. This study investigates the mechanisms by which the visual environment of urban micro public spaces shapes residents’ psychophysiological responses to encourage spontaneous physical activity and advance active health. Using machine learning and semantic segmentation, 9 core visual elements across 20 micro public space scenes in high-density urban neighborhoods were quantified. An immersive virtual reality (VR) experiment was conducted, collecting synchronized multimodal psychophysiological data from 60 participants, which yielded 600 valid observations. Through an analytical framework combining Self-Organizing Map (SOM) clustering and Random Forest (RF) modeling, three distinct functional archetypes were identified: Restoration-Supporting, Activity-Promoting, and Stress-Inducing. The Activity-Promoting archetype was most effective in fostering spontaneous activity intention, characterized by a high proportion of activity areas, a moderate sky view factor, and minimal physical barriers. RF modeling further pinpointed pedestrian density, activity area ratio, and green space ratio as key visual drivers of health-promoting outcomes. Based on these findings, a “Visual Activation for Active Health” framework is proposed. It posits that moderate visual-environmental stimulation is the core mechanism for transforming passive spaces into health-promotive settings, thereby establishing a theoretical foundation for the evidence-based design of healthy and sustainable urban environments. Full article

Rapid detection and follow-up of electromagnetic (EM) counterparts to gravitational wave (GW) signals from binary neutron star (BNS) mergers are essential for constraining source properties and probing the physics of relativistic transients. Observational strategies for these early EM searches are therefore critical, yet current practice remains suboptimal, motivating improved, coordination-aware approaches. We propose and evaluate the Two-Step Localization strategy, a coordinated observational protocol in which one wide-field auxiliary telescope and one narrow-field main telescope monitor the evolving GW sky localization in real time. The auxiliary telescope, by virtue of its large field of view, has a higher probability of detecting early EM emission. Upon registering a candidate signal, it triggers the main telescope to slew to the inferred location for prompt, high-resolution follow-up. We assess the performance of Two-Step Localization using large-scale simulations that incorporate dynamic sky-map updates, realistic telescope parameters, and signal-to-noise ratio (SNR)-weighted localization contours. For context, we compare Two-Step Localization to two benchmark strategies lacking coordination. Our results demonstrate that Two-Step Localization significantly reduces the median detection latency, highlighting the effectiveness of targeted cooperation in the early-time discovery of EM counterparts. Our results point to the most impactful next step: next-generation faster telescopes that deliver drastically higher slew rates and shorter scan times, reducing the number of required tiles; a deeper, truly wide-field auxiliary improves coverage more than simply adding more telescopes. Full article

Urban morphology plays a decisive role in regulating microclimate and outdoor thermal comfort in arid cities, where extreme heat and intense solar radiation amplify thermal stress. This study examines the influence of four contrasting urban fabrics in Béchar (Algerian Sahara): the vernacular Ksar, the regular-grid colonial fabric, a modern large-scale residential estate, and low-density detached housing, on local microclimatic conditions. An integrated methodological framework is adopted, combining qualitative morphological analysis, quantitative indicators including density, porosity, height-to-width ratio, and sky view factor, in situ microclimatic measurements, and high-resolution ENVI-met simulations performed for the hottest summer day. Results show that compact urban forms, characterized by low sky view factor values, markedly reduce radiative exposure and improve thermal performance. The vernacular Ksar, exhibiting the lowest SVF, records the lowest mean radiant temperature (approximately 45 °C) and the most favorable average comfort conditions (PMV = 3.77; UTCI = 38.37 °C), representing a reduction of about 3 °C, while its high-thermal-inertia earthen materials ensure effective nocturnal thermal recovery (PMV ≈ 1.06; UTCI = 27.8 °C at 06:00). In contrast, more open modern fabrics, including the colonial grid, large-scale estates, and low-density housing, experience higher thermal stress, reflecting vulnerability to solar exposure and limited thermal inertia. Validation against field measurements confirms model reliability. These findings highlight the continued relevance of vernacular bioclimatic principles for sustainable urban design in arid climates. Full article

The Daylight Glare Probability (DGP) includes the luminance of a glare source quadratically, but the solid angle only linearly. While this is in line with formulae of other glare metrics, it must be questioned for small glare sources, if the glare stimulus can no longer be distinguished from larger stimuli causing equal vertical illuminance at the eye, especially in the peripheral visual field. To account for this, the modified version Daylight Glare Metric (DGM) was previously developed. We conducted two studies to evaluate the effect of the modified DGM. First, in a laboratory study under an artificial sky with an LED sun, 35 test subjects evaluated different glare situations. Second, we performed a comprehensive simulation study for an office space, including three locations, three view directions, and 17 window systems (electrochromic glazing, fabric shades). The results from the perception study under the artificial sky provide evidence that the adapted DGM is better suited to predict glare from small, bright sources. The results from the simulation study for a realistic office setting show that, compared to the DGP, the DGM reduces glare ratings for many hours of the year, thus underscoring the practical relevance of improving the DGP formula. Full article

Historic arcades in shaded street canyons may act as passive microclimate infrastructure. We monitored paired arcade–open points along two arcaded streets in Haikou, China, under clear summer conditions, recording hourly microclimate from 09:00 to 21:00. From these data we derived mean radiant temperature (Tmrt) and the Universal Thermal Climate Index (UTCI), tested main and interaction effects of space (arcade vs. open) and orientation (east–west vs. north–south), examined relations with sky view factor (SVF), and quantified exceedances of health-relevant thresholds using wet-bulb globe temperature (WBGT) and degree-hours. Arcades consistently lowered thermal exposure, with the largest benefits around midday–afternoon; the daily mean UTCI reduction was ~4.4 °C relative to adjacent open points. Orientation modulated benefits: east–west segments showed larger marginal reductions, and orientation differences were markedly compressed beneath arcades. SVF correlated positively with Tmrt and thermal stress but contributed little additional explanatory power after accounting for space and orientation, indicating geometric shielding as a primary mechanism. High-risk WBGT windows (≥32 °C) were strongly reduced under arcades, and day–night degree-hour summaries indicated net improvement. We conclude that historic arcades provide measurable thermal protection while preserving urban form, supporting their dual role as cultural heritage and passive climate-adaptation assets. Full article

Semantic segmentation of crowdsourced street-level imagery plays a critical role in urban analytics by enabling pixel-wise understanding of urban scenes for applications such as walkability scoring, environmental comfort evaluation, and urban planning, where robustness to geometric transformations and projection-induced symmetry variations is essential. This study presents a comparative evaluation of two primary families of semantic segmentation models: transformer-based models (SegFormer and Mask2Former) and prompt-based models (CLIPSeg, LangSAM, and SAM+CLIP). The evaluation is conducted on images with varying geometric properties, including normal perspective, fisheye distortion, and panoramic format, representing different forms of projection symmetry and symmetry-breaking transformations, using data from Google Street View and Mapillary. Each model is evaluated on a unified benchmark with pixel-level annotations for key urban classes, including road, building, sky, vegetation, and additional elements grouped under the “Other” class. Segmentation performance is assessed through metric-based, statistical, and visual evaluations, with mean Intersection over Union (mIoU) and pixel accuracy serving as the primary metrics. Results show that LangSAM demonstrates strong robustness across different image formats, with mIoU scores of 64.48% on fisheye images, 85.78% on normal perspective images, and 96.07% on panoramic images, indicating strong semantic consistency under projection-induced symmetry variations. Among transformer-based models, SegFormer proves to be the most reliable, attains higher accuracy on fisheye and normal perspective images among all models, with mean IoU scores of 72.21%, 94.92%, and 75.13% on fisheye, normal, and panoramic imagery, respectively. LangSAM not only demonstrates robustness across different projection geometries but also delivers the lowest segmentation error, consistently identifying the correct class for corresponding objects. In contrast, CLIPSeg remains the weakest prompt-based model, with mIoU scores of 77.60% on normal images, 59.33% on panoramic images, and a substantial drop to 59.33% on fisheye imagery, reflecting sensitivity to projection-related symmetry distortions. Full article

Vegetation phenology significantly influences urban microclimate and thermal comfort in cold regions, yet its quantitative impact—specifically the potential of deciduous trees to enhance winter solar access—remains underexplored. This study investigates how seasonal vegetation changes affect thermal conditions in an urban plaza. Field measurements were conducted at Beiyang Plaza, Tianjin University, during the autumn–winter transition. High-precision Sky View Factors (SVF) were extracted from panoramic images using a deep learning-based semantic segmentation model (PSPNet), validated against field observations. The Universal Thermal Climate Index (UTCI) was calculated to assess thermal stress. Results indicate that the leaf-off phase significantly increases SVF, shifting the radiative balance. Areas experiencing phenological changes exhibited a marked improvement in UTCI, effectively alleviating cold stress by maximizing solar gain. Advanced statistical models (ARIMAX and GAM) confirmed that, after controlling for background climatic variations, the positive effect of vegetation phenology on thermal comfort is statistically significant. These findings challenge the traditional focus on summer shading, highlighting the “winter-warming” potential of deciduous trees and providing quantitative evidence for climate-responsive urban design. Full article

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration. Full article

Understanding outdoor thermal environments at fine spatial scales is essential for developing climate-responsive urban and building design strategies. This study investigates the determinants of local air temperature deviations in Seoul, Korea, using high-resolution in situ sensor data integrated with multi-source urban and building information. Hourly temperature records from 436 road-embedded sensors (March 2024–February 2025) were transformed into relative metrics representing deviations from the network-wide mean and were combined with semantic indicators derived from street-view imagery—Green View Index (GVI), Road View Index (RVI), Building View Index (BVI), Sky View Index (SVI), and Street Enclosure Index (SEI)—along with land-cover and building attributes such as impervious surface area (ISA), gross floor area (GFA), building coverage ratio (BCR), and floor area ratio (FAR). Employing an eXtreme Gradient Boosting (XGBoost)–Shapley Additive exPlanations (SHAP) framework, the study quantifies nonlinear and interactive relationships among morphological, environmental, and visual factors. SEI, BVI, and ISA emerged as dominant contributors to localized heating, while RVI, GVI, and SVI enhanced cooling potential. Seasonal contrasts reveal that built enclosure and vegetation visibility jointly shape micro-scale heat dynamics. The findings demonstrate how high-resolution, observation-based data can guide climate-responsive design strategies and support thermally adaptive urban planning. Full article

Urban waterfront spaces are key venues for residents’ physical activity, and their spatial environment significantly impacts usage efficiency. Existing studies predominantly employ linear models and focus on single waterfront types, making it difficult to reveal differences across various types and the nonlinear mechanisms of influencing factors. To address this, this study investigates three types of waterfront spaces in Shenzhen—riverfront, lakeshore, and seafront spaces—integrating multi-source data and machine learning techniques to systematically analyze the differential impacts of the same elements on physical activity. The results indicate: (1) In terms of transportation accessibility, public transport is the most important factor for riverfront and lakeshore spaces, while road network accessibility is most critical for seafront spaces. (2) Regarding natural landscapes, the dominant factors are normalized difference vegetation index (NDVI) for riverfront spaces, green view index for lakeshore spaces, and distance to the shoreline for seafront spaces. (3) For facility services, the core factors are building density (riverfront), number of sports facilities (lakeshore), and number of leisure facilities (seafront). (4) The study further reveals nonlinear relationships and threshold effects of multiple elements. For instance, a turning point in physical activity intensity occurs when the distance to a subway station reaches 2–2.5 km. The green view index shows a threshold of 30% in the overall model, while dual-threshold phenomena are observed in the lakeshore and seafront models. (5) Synergistic effects between elements vary by waterfront type: in riverfront and seafront spaces, activity is more vibrant when areas are close to subway stations and have a low sky view index, whereas the opposite pattern is observed in lakeshore spaces. A combination of a high green view index and greater distance to the shoreline promotes activity in lakeshore spaces, while a high green view index combined with proximity to the shoreline has the most significant promotional effect in riverfront and seafront spaces. This study provides a scientific basis for health-oriented, precise planning and design of urban waterfront spaces. Full article