Search Results (74)

Rapid urbanization and unplanned land development have transformed Dhaka into one of the most densely built metropolitan areas in South Asia, leading to increased surface heat accumulation and growing pressure on urban energy systems. Numerous studies have reported a progressive rise in Dhaka’s Land Surface Temperature (LST), accompanied by a strong negative correlation between the Normalized Difference Vegetation Index (NDVI) and LST and a positive correlation between the Normalized Difference Built-Up Index (NDBI) and LST. However, spatially explicit assessments that integrate multiple geospatial proxies to characterize urban energy-intensity patterns remain limited, particularly in data-scarce cities where direct energy-consumption data are unavailable. The present study aims to develop and map a proxy-based Urban Energy Intensity Index (UEI) for Dhaka by integrating remote sensing, GIS, and AHP-MCDA techniques. Landsat 9 imagery and VIIRS nighttime light data were processed to derive NDVI, NDBI, and LST layers, while building footprints and road networks were extracted from OpenStreetMap to represent urban form. Eight environmental, built-environment, and anthropogenic indicators were standardized and weighted using the Analytic Hierarchy Process (AHP) before integration through a GIS-based weighted overlay approach. Results show that dense, impervious, and intensely illuminated built-up cores exhibit elevated LST and UEI values, while peri-urban and vegetated areas consistently display lower values, producing spatially distinct high-UEI clusters concentrated within Dhaka’s urban core. Because the index is derived from proxy indicators and was not validated against observed energy-consumption data, the findings should be interpreted as relative spatial patterns rather than direct measures of energy use or energy efficiency. These findings demonstrate the effectiveness of integrating remotely sensed and geospatial indicators to capture intra-urban variability in proxy-based urban energy intensity. The study provides a scalable and data-efficient framework for identifying priority zones for urban planning interventions and supports targeted strategies such as urban greening, surface albedo enhancement, and sustainable land-use planning to mitigate thermal stress. Full article

Open-world person re-identification (ReID) faces severe spatially asymmetric interferences, such as partial occlusion and illumination distortion. Existing models adopting static weight fusion irreversibly corrupt identity representations when processing localized noise. To address this, we propose a Scene-Prompt-Driven Dynamic Routing Expert Network (SPDR-Net). Guided by spatial topological prior constraints, SPDR-Net treats person semantic parts as independent local experts. First, a semantic expert module decouples and refines local features using human spatial topology priors. Second, a Prompt-Guided Dynamic Routing (PGDR) network extracts high-level scene contexts to dynamically evaluate each expert’s reliability, assigning routing weights to attenuate noise propagation. Finally, a global–local fusion module superimposes high-purity local features onto a global identity anchor, maintaining topological integrity to generate a unified descriptor for robust person re-identification. Extensive experiments on seven public benchmarks and a newly constructed real-world street dataset (SD-ReID) demonstrate that SPDR-Net achieves highly competitive performance against state-of-the-art methods and exhibits superior robustness under severe spatial-asymmetric interferences. Furthermore, end-to-end multi-camera closed-loop tests verify its robust decision-making capability and high engineering application value in real-world security systems. Full article

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

In Guangzhou’s Shangxiajiu Pedestrian Street, street segments with higher billboard densities exhibited greater proportions of both brief stops and extended dwell, while segments with higher densities of illuminated signage at night also showed greater proportions of extended dwell. Higher street width-to-height ratios and greater ground-floor façade transparency were associated with increased afternoon pedestrian activity and brief stopping, respectively. Using 13 street segments as the units of analysis, this study combined on-site measurements, synchronized behavioral observations, and correlation analysis to examine the associations of billboard density, illuminated signage density, street width-to-height ratio, and façade transparency with brief stopping, extended dwell, and through-movement behavior. The results showed that billboard density was strongly and positively correlated with both the mean proportion of brief stops and the mean proportion of extended dwell (r = 0.749, p = 0.003; r = 0.832, p < 0.001). Illuminated signage density was also strongly and positively correlated with the proportion of extended dwell at night (r = 0.823, p = 0.001). Street width-to-height ratio was positively correlated with both the afternoon proportion of brief stops and the afternoon proportion of total pedestrian activity, while façade transparency was positively correlated with the afternoon and mean proportions of brief stops. These findings indicate that, in the case of Shangxiajiu Pedestrian Street, pedestrian dwell was more concentrated in street segments characterized by richer commercial information, greater nighttime visibility, more open spatial configurations, or more legible storefront interfaces. The findings provide field-based quantitative evidence to inform signage layout, ground-floor interface optimization, the coordination of street enclosure, and the management of nighttime visual environments in historic commercial streets. Full article

The main target of street and road lighting is to ensure the safety and the well-being of pedestrians and drivers. Regulations and standards on lighting installations establish minimum photometric requirements to achieve it. Thus, the main parameters concern the average luminance or illuminance, overall uniformity, longitudinal uniformity, threshold increment, edge illuminance ratio, and minimum energy efficiency or its equivalent. Although they have well-defined minimum and maximum values, their compliance, especially in urban and peri-urban environments, strongly depends on the heterogeneous characteristics of the street and its surroundings, depends on human physiological and psychological aspects, and/or faces remarkable uncertainties and problems of definition. The coefficient of utilization, C_u, and energy efficiency, ε, are key quantifiers taking account of the installation capability to provide luminous flux on the visual work plane with respect to the flux emitted and the power consumed by the light sources, respectively. However, contradictions between accurate values of these coefficients and the real visual performance of people or the rational use of energy are frequent. This is a problem because the binomial safety–sustainability requires consideration of these parameters in the design to enhance pedestrian and driver safety, as well as energy efficiency for sustainability. This work highlights the uncertainties and limitations of C_u and ε, redefines them through a human-centered approach that opens new perspectives on other parameters and quantities involved in lighting, and optimizes the binomial safety–sustainability. Full article

This paper addresses the issue of lighting in historic urban spaces, using Tarnow (Poland) as a case study. The aim is to assess the impact of artificial light sources on visual comfort within the area, with particular consideration given to light pollution. A comprehensive inventory of active street lighting in the Old Town was conducted. Measurements taken at ground and eye level revealed strong inconsistencies: some areas were under-lit (<1 lx), while others showed façade illuminance above 100 lx, far exceeding recommended thresholds. The highest environmental impact was shown by decorative and globe-type fixtures, with Sky Glow Contribution Index (SGCI) values of up to 0.62. Only suspended street luminaires met CIE requirements (ULR ≤ 15%). The findings reveal that several lighting installations do not meet recommended standards, adversely affecting both human comfort and ecological balance. The study proposes strategies to optimise urban lighting, such as replacing inefficient fixtures with full cut-off LED luminaires and implementing intelligent lighting control systems which could reduce energy consumption by 50-67% while preserving the architectural character of the historic centre. The results provide evidence-based strategies for sustainable lighting modernisation in heritage cities across Europe. Full article

Pulse-width-modulated (PWM) automotive headlights enhance nighttime event-based camera detection, yet systematic parameter optimization for vulnerable road user detection remains unexplored. This study evaluates PWM frequency, duty cycle, light distribution, ego-vehicle speed, and ambient lighting under European New Car Assessment Programme-inspired crossing scenarios for cyclist and pedestrian detection. Results establish performance ranging from substantial improvements to severe degradation relative to continuous illumination. Cyclist detection achieves robust performance with high-frequency modulation across light distributions, while low-frequency operation with low beam produces severe degradation through background noise accumulation. Pedestrian detection requires high beam with street lighting enabled; low beam universally fails regardless of modulation parameters. Limited parameter combinations achieve simultaneous improvements for both targets. Detection performs optimally on retroreflective surfaces, while low-reflectivity clothing limits capability, requiring target-specific optimization. Full article

Characterizing the spectral composition of artificial light at night (ALAN) within urban green spaces (UGS) is vital for ecological conservation, yet traditional sensors often lack the requisite spatial and spectral resolution for fine-scale analysis. To address this gap, this study leverages high-resolution multispectral nighttime light (NTL) data from the SDGSAT-1 to perform a fine-scale characterization of lighting across diverse UGS typologies. We developed UGS-STUNet, a semantic segmentation framework based on Swin Transformer architecture, to accurately extract five UGS categories from Google Earth imagery. Two specialized spectral indices, blue-to-green (B/G) and green-to-red (G/R) ratios, were derived from SDGSAT-1 NTL data to quantify the lighting’s spectral composition. Application in Shanghai demonstrated that UGS-STUNet achieved a precision of 85.72%, significantly outperforming existing methods. Our findings reveal that street trees are subjected to the highest red-light intensity and the lowest B/G and G/R ratios due to their proximity to roadway illumination. In contrast, forest patches and belts exhibit higher spectral ratios, indicating a relatively higher exposure to blue and green wavelengths. This study provides a robust and scalable method for monitoring the spectral quality of urban nightscapes, offering critical insights for sustainable urban planning and lighting mitigation strategies to safeguard global biodiversity and public health. Full article

Urban lighting systems contribute significantly to energy consumption and light pollution, raising environmental and societal concerns. This paper explores the potential of Artificial Intelligence (abbreviation: AI)-driven adaptive urban lighting as a sustainable solution, framed within a multi-level perspective on socio-technical transitions. At the landscape level, increasing urbanization and global sustainability targets exert pressure for energy-efficient practices, while traditional street lighting regimes remain largely rigid and resource-intensive. At the niche level, we propose a novel adaptive lighting system integrating real-time Internet of Things (abbreviation: IoT) sensor data and machine learning algorithms to dynamically adjust illumination based on traffic, pedestrian activity, weather conditions, and ambient light. Studies demonstrate that the proposed approach can significantly reduce energy use while minimizing light pollution, without compromising safety or visibility. The results indicate that such niche innovations, supported by AI and renewable energy integration, have the potential to influence broader regime change and contribute to sustainable urban development. This research highlights the importance of combining technological innovation with socio-technical frameworks to address pressing urban environmental challenges, offering insights for policymakers, urban planners, and energy managers seeking to balance efficiency, safety, and ecological impact. Full article

Glare is a critical factor in the design of LED luminaires for street lighting, particularly in environments where pedestrians, cyclists and drivers coexist. Generally, glare assessments are performed for fixed geometries and a single observer, limiting their applicability to real urban environments. This study examines the effect of angular redistribution of the beam on glare and illuminance by introducing the relative angular parameter α into the photometric model and the UGR calculation. A generic LED luminaire is modelled using a cosine-type luminous intensity distribution raised to a power, and the emitting surface is also discretized to evaluate the luminance, solid angle and Guth position index at the patch level. This approach is applied to three distinct observer geometries—pedestrian, cyclist and driver—allowing direct comparison using a unified mathematical formulation. The results show that beam redistribution affects each observer differently, reducing glare for pedestrians while simultaneously increasing it for drivers, whereas cyclists show limited sensitivity to angular changes. Although relative illuminance and UGR show similar monotonic trends, their physical and perceptual interpretation is different. This paper presents a novel tool for the preliminary analysis of trade-offs between visual comfort and luminous efficiency in urban lighting design. Full article

Background. Exposure to artificial light at night can lead to circadian disruption and health risks. It can cause mood swings, confusion, and depression. The aim of this cross-sectional study was to assess the relationship between the illuminance of urban lighting and the health of residents. Methods: This study was carried out among residents of two similar towns, one with typical street lighting and a Dark Sky Park characterized by reduced lighting. A total of 272 respondents participated in this study. A self-administered questionnaire and the WHOQOL-BREF were used among the respondents. Results. People living in the Dark Sky Park were more likely to be satisfied with their sleep (p < 0.001). In fact, 58.7% of Dark Sky Park residents reported no sleep problems. In the control town, only 49.25% did (p = 0.04). The sleep duration was similar in the two towns, but Dark Sky Park residents were statistically less likely to use sleeping pills and window blinds. People exposed to typical street lighting at night reported suffering from eye diseases, cardiovascular diseases, and mood changes more often than those living in the Dark Sky Park. The environmental and physical quality of life, as measured by the WHOQOL-BREF, were significantly higher in the Dark Sky Park residents than in the control town (p < 0.05). Conclusions. People living in places with limited illuminance declare better health and a higher quality of life in the physical and environmental domains. Full article

This study proposes an intelligent surveillance framework that integrates image preprocessing, illuminance-adaptive object detection, multi-object tracking, and pedestrian abnormal behavior recognition to address the rapid degradation of image recognition performance under low-illuminance street lighting conditions. In the preprocessing stage, image quality was enhanced by correcting color distortion and contour loss, while in the detection stage, illuminance-based loss weighting was applied to maintain high detection sensitivity even in dark environments. During the tracking process, a Kalman filter was employed to ensure inter-frame consistency of detected objects. In the abnormal behavior recognition stage, temporal motion patterns were analyzed to detect events such as falls and prolonged inactivity in real time. The experimental results indicate that the proposed method maintained an average detection accuracy of approximately 0.9 and adequate tracking performance in the 80% range under low-illuminance conditions, while also exhibiting stable recognition rates across various weather environments. Although slight performance degradation was observed under dense fog or highly crowded scenes, such limitations are expected to be mitigated through sensor fusion and enhanced processing efficiency. These findings experimentally demonstrate the technical feasibility of a real-time intelligent recognition system for nighttime street lighting environments. Full article

To enact urban resilience at the street–neighborhood scale, we advance a two-stage “measurement–scenario–combination” framework. Stage 1 develops and validates a unified instrument covering four latents—place attachment/identity, accessibility–safety, governance–maintenance, and adoption–participation. Stage 2 uses an image-based conjoint with seven street-landscape elements at five levels; utilities are estimated with hierarchical Bayes, and multigroup SEM with bootstrapped mediation (public vs. expert) tests psychosocial pathways via perceived safety, place attachment, and governance beliefs. The sampling blends online self-administration with targeted invitations under quotas and quality controls. The results yield transferable thresholds and consensus anchors: street width and lighting peak in the upper-middle range; greenery and activity hubs follow inverted-U curves; and preferred traffic exposure centers on mid-to-low bands. Mediation is stronger through attachment/safety for the public, while experts rely more on governance/maintenance beliefs; disagreements concentrate at upper extremes (over-illumination and excessive canopy). We contribute a deployable configuration frontier that translates “being seen–being shaded–being used” into governable specifications, integrating public–expert knowledge to support citywide baselines, community negotiation menus, and policy–standard updates for heat- and injury-risk mitigation and activation of use and collaboration. Full article

Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift toward automated, accurate, and scalable monitoring systems capable of quickly identifying malfunctioning streetlights. In response, this study introduces an advanced computer vision-based approach for automated detection and analysis of street lighting conditions. Leveraging high-resolution dashcam footage collected under diverse nighttime weather conditions, we constructed a robust dataset of 4260 carefully annotated frames highlighting streetlight poles and lamps. To significantly enhance detection accuracy, we propose the novel YOLO-CSE model, which integrates a Channel Squeeze-and-Excitation (CSE) module into the YOLO (You Only Look Once) detection architecture. The CSE module leverages the inherent symmetry of streetlight structures, such as the bilateral symmetry of poles and the radial symmetry of lamps, to dynamically recalibrate feature channels, emphasizing spatially repetitive and geometrically uniform patterns. By modifying the bottleneck layer through the addition of an extra convolutional layer and the SE block, the model learns richer, more discriminative feature representations, particularly for small or distant lamps under partial occlusion or low illumination. A comprehensive comparative analysis demonstrates that YOLO-CSE outperforms conventional YOLO variants and state-of-the-art models, achieving a mean average precision (mAP) of 0.798, recall of 0.794, precision of 0.824, and an F1 score of 0.808. The model’s symmetry-aware design enhances robustness to urban clutter (e.g., asymmetric noise from headlights or signage) while maintaining real-time efficiency. These results validate YOLO-CSE as a scalable solution for smart cities, where symmetry principles bridge geometric priors with computational efficiency in infrastructure monitoring. Full article

Sustainability has an ever-increasing importance in our lives, mainly due to climate changes, finite resources, and a growing population, where each of us is called to make a change. Although climate change is a global phenomenon, our individual choices can make the difference. The transportation sector is one of the largest contributors to global carbon emissions, making the transition toward sustainable mobility a critical priority. The adoption of electric vehicles is widely recognized as a key solution to reduce the environmental impact of transportation. However, their widespread acceptance depends on various technological, behavioral, and economical factors. Within this research we use as an artifact the CO₂ Emission Management Gauge (CEMG) devices to better understand how the manufacturers, with integrated features on vehicles, could significantly enhance sales and drive the movement towards electric vehicle adoption. This study proposes an innovative new theoretical model based on Task-Technology Fit, Technology Acceptance, and the Theory of Planned Behavior to understand the main drivers that may foster electric vehicle adoption, tested in a quantitative study with structural equation modelling (SEM), and conducted in a South European country. Our findings, not without some limitations, reveal that while technological innovations like CEMG provide consumers with valuable transparency regarding emissions, its influence on the intention of adoption is dependent on the attitude towards electric vehicles and subjective norm. Our results also support the influence of task-technology fit on perceived usefulness and perceived ease-of-use, the influence of perceived usefulness on consumer attitude towards electric vehicles, and the influence of perceived ease-of-use on perceived usefulness. A challenge is also presented within our work to expand CEMG usage in the future to more intrinsic urban contexts, combined with smart city algorithms, collecting and proving CO₂ emission information to citizens in locations such as traffic lights, illumination posts, streets, and public areas, allowing the needed information to better manage the city’s quality of air and traffic. Full article