Search Results (1,440)

Urban vitality (UV) is the core index to measure the quality and sustainability of urban development. Accurately analyzing the complex association mechanism between UV and built environment (BE) is critical to urban planning practice. Focusing on the main urban area of Chengdu, this study integrates eight categories of multi-source data, including nighttime light data, WorldPop population distribution data, street view images, and POI data, to construct a four-dimensional UV evaluation system and identify 26 BE factors. Firstly, the UV level is quantified by objective weighting methods. Secondly, an XGBoost model combined with a SHAP framework is adopted to investigate the nonlinear association between UV and BE factors. Finally, a spatial autocorrelation model, SHAP spatial visualization and clustering methods are employed to reveal the spatial pattern of UV and the spatial heterogeneity of the association between UV and BE. The results indicate: (1) Various elements of the BE show a significant nonlinear association and threshold effect for UV. Catering services and public transit services are the core factors for UV prediction, with their combined contribution accounting for 37.47%. (2) UV shows obvious spatial differentiation and agglomeration characteristics. It presents a spatial pattern with a gradual decline from the core to the periphery. (3) The association between UV and BE presents spatial heterogeneity, and the predictive contribution logic differs distinctly across different concentric rings. The study conclusions provide a scientific basis for UV improvement and BE optimization in Chengdu. Full article

Multi-object tracking (MOT) in low-light environments presents significant real-world application value. Despite substantial progress in MOT, low-light MOT remains constrained by the scarcity of specialized datasets, largely because collecting and manually annotating low-light tracking data is difficult and often prohibitively expensive. This paper specifically addresses these challenges through methodological and dataset innovations. We first present low-light multi-object tracking (LLMOT), the first comprehensive low-light MOT dataset containing 11,580 images, including 5316 labeled and 6264 unlabeled images, consisting of nighttime-enhanced MOT17 sequences and multiple unannotated low-light videos. To simultaneously alleviate the constraint of annotation costs and address the damage that low-light-induced image degradation causes to pseudo-label quality, we propose Consistency Regularization Track (CRTrack), a semi-supervised framework tailored for low-light scenarios. Specifically, we introduce a consistent adaptive sampling assignment mechanism that calibrates and filters noisy and shifted pseudo-bounding boxes under low-illumination conditions. We then design an adaptive semi-supervised network update strategy that enables the model to more stably exploit unlabeled low-light videos for iterative optimization. Extensive experiments on the LLMOT dataset validate the effectiveness and robustness of the proposed method. CRTrack achieves 62.472 HOTA, 71.544 MOTA, and 75.864 IDF1 on the LLMOT dataset, demonstrating its effectiveness in low-light MOT. Our approach provides a practical solution for low-light MOT tasks with significant real-world implications. Full article

Urban–rural transition zones (URTZs) represent critical spatial units where urban expansion interacts with ecosystems. In arid regions, however, the response of ecological environmental quality (EEQ) to rapid urban expansion and spatial restructuring remains insufficiently understood. Taking Xinjiang as a representative arid-region case, this study develops an analytical framework integrating dynamic URTZs identification, EEQ assessment, and the analysis of influencing factors and nonlinear responses to systematically investigate URTZs expansion and EEQ changes across 13 typical urban agglomerations from 2002 to 2022. URTZs were identified using K-means clustering by integrating population density, nighttime light intensity, and impervious surface information. An improved remote sensing ecological index (ARSEI) was then developed by incorporating the abundance index (AI) into the traditional RSEI framework. Finally, XGBoost and SHAP were employed to identify the key determinants of EEQ and reveal their nonlinear responses and interactions. The results showed that: (1) URTZs expanded rapidly and continuously from 2002 to 2022, with their total area increasing by more than threefold and exhibiting a spatial restructuring pattern characterized by expansion from central cities toward multiple nodes. (2) Despite the rapid expansion of URTZs, overall EEQ remained at a relatively high level; however, the grade structure exhibited a trend of “expansion at both ends and contraction in the middle,” intensifying the spatial differentiation of EEQ. (3) XGBoost and SHAP analyses identified precipitation (PRE), population density (POP), digital elevation model (DEM), and slope as major factors explaining the spatial variation in EEQ. Interaction analysis further revealed strong interactions between PRE × DEM and PRE × POP. High EEQ values were primarily distributed in areas characterized by favorable precipitation conditions, moderate elevations, and gentle terrain, indicating that the synergistic effects of hydrothermal conditions and topographic constraints play a significant role in shaping EEQ in URTZs. These findings demonstrate that rapid URTZs expansion in arid regions does not necessarily lead to an overall decline in EEQ but may intensify its spatial differentiation. Therefore, ecological governance of URTZs should shift from a singular focus on controlling urban expansion toward differentiated spatial management that jointly considers hydrothermal conditions, topographic constraints, population concentration, and ecological carrying capacity, thereby promoting coordinated urbanization and ecological conservation. Full article

Pedestrian safety at urban intersections remains a major challenge in Intelligent Transportation Systems (ITSs). This study investigates whether crossing intention can be reliably inferred directly from temporal body-pose dynamics to drive real-time collision warnings on embedded edge platforms. Existing vision-based approaches that rely primarily on bounding-box proximity or scene-level spatial grids are often prone to false alarms in complex urban environments with motorcycles, stationary pedestrians, and background clutter. To overcome these limitations, we propose an end-to-end framework consisting of four sequential processing stages: (1) a perception layer integrating YOLOv8s, ByteTrack, a displacement filter, and rider suppression to generate reliable pedestrian trajectories; (2) a skeleton extraction layer utilizing YOLOv8s-pose to construct temporal sequences of 17 anatomical keypoints; (3) an ultra-lightweight Skeleton Graph Convolutional Network (SkeletonGCN, comprising 33.8 K parameters, <0.2 MB) that models body-joint kinematics and temporal motion dynamics; and (4) an image-space Time-to-Collision (TTC) risk-fusion module. While this fusion approach avoids explicit geometric camera calibration, it still relies on predefined scene-profile parameters and image-space motion assumptions. Furthermore, while the intention classifier is quantitatively evaluated, the risk-fusion module is procedurally defined, and its resulting four-level collision warnings are demonstrated operationally rather than validated against ground-truth hazard annotations. Evaluated on 49,948 valid sequences from the JAAD and PIE benchmark datasets under a strict video-level partitioning protocol, the unified SkeletonGCN achieves a macro-F1 score of 0.717 (with per-scene subset macro-F1 scores of 0.761 on JAAD/PIE urban and 0.895 on intersections), significantly outperforming baseline models. When deployed on an NVIDIA Jetson Orin NX edge device using TensorRT FP16, the full pipeline achieves an instrumented latency of 70.7 ms per frame (~14 fps) and a sustained wall-clock throughput of 7.4 fps on real-world urban dashcam video. System limitations include sensitivity to 2D printed human imagery and reduced prediction reliability under low-light nighttime conditions. Full article

Neuroendocrine and circadian dysfunctions are thought to underlie bipolar disorder (BD) and could be implemented as markers of complex symptom conditions, including the presence of Post-Traumatic Stress Disorder (PTSD) comorbidity. The primary objective of this study was to investigate neuroendocrine profiles in different BD phenotypes through the measure of nocturnal urinary levels of Cortisol (stress-response factor) and 6-sulfatoxymelatonin (aMT6s; light/dark signal) in euthymic BD patients diagnosed with PTSD (PTSD group) or with major depressive episodes without trauma symptoms (DEP group). Both groups were compared against healthy controls (CTL). Nighttime urinary levels of aMT6s and Cortisol were analyzed by competitive ELISA, with ensuing values normalized for specific gravity. The Cortisol-to-aMT6s ratio (Cort/aMT6s) was calculated as an exploratory proxy for the homeostatic balance between HPA-mediated catabolic/coping activities and Melatonin-related reparative functions. Clinical severity and psychosocial activities were assessed by using mood HAM-D, YMRS, trauma IES-R and functioning WSAS scales. Results revealed distinct biological patterns: PTSD subjects showed increased nocturnal Cortisol compared to DEP and CTL groups, whereas DEP patients exhibited lower aMT6s levels and an elevated Cort/aMT6s ratio. Cortisol and aMT6s correlated positively in PTSD, while the ratio correlated positively with Cortisol solely in controls. Clinically, Cortisol correlated positively with IES-R and functional impairment (WSAS), whereas aMT6s correlated negatively with HAM-D and YMRS scores. Present findings suggest a possible neuroendocrine divergence in BD patients with depression versus PTSD: nocturnal Melatonin deficiency may be associated with depression, while HPA-axis hyperactivity and altered crosstalk between the two systems may characterize PTSD comorbidity, with the Cort/aMT6s ratio as a potential index of impaired daily functioning. These results could be promising for the use of biomarkers linked to allostatic and circadian responses within the BD spectrum. Full article

Background: Artificial Light at Night (ALAN) is a growing public health concern, associated with circadian disruption, sleep disturbances, mental health issues, and increased risk for chronic conditions such as cancer and cardiovascular diseases. Despite a rapidly expanding epidemiological literature on ALAN-related health outcomes, there is limited consensus on standardized and comparable tools for assessing both how individuals perceive ALAN and their objective exposure at the individual and population levels. Methods: A scoping review was conducted using Scopus, PubMed, and Web of Science to identify studies published between 2010 and 2025. The aim was to systematically map the methods, tools, and frameworks used to evaluate both the perception of ALAN and objective ALAN exposure, and to examine their implications for public health. Eligibility was restricted to studies explicitly describing approaches to assess ALAN perception or exposure applied to human health research. Results: Most studies adopted cross-sectional designs and focused on urban contexts. Satellite-derived nighttime light data (62%) and GIS-based indicators (46%) were the most frequently used exposure proxies, while surveys and self-reported measures were commonly used to capture individual behaviors, health impacts and perceptions. Only 23% of studies employed wearable sensors or mobile applications, and architectural or building-level data were included in a single study (8%), highlighting a substantial gap in indoor exposure characterization. Conclusions: By critically reviewing current methods, tools, and frameworks, this work highlights the urgent need for harmonized, validated approaches that integrate objective environmental light measurements with human-centered data on how ALAN is perceived and experienced, thereby strengthening the evidence base for public health research and policy. Full article

Urban vitality matters for urban regeneration, yet built-environment–vitality associations vary spatially and by variable. We examined 2621 regular 500 m grid cells across Changchun’s built-up area. PCA combined 2024 population, 2024 nighttime light, and 2018 mean building height into a composite Urban Vitality index. Five built-environment indicators—Functional Density, POI Diversity, Mean NDVI, Transit Proximity, and Commercial Proximity—were analyzed using Global Moran’s I, ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). PC1 explained 82.15% of the variance, and Urban Vitality showed strong spatial autocorrelation (Moran’s I = 0.8332). Model fit increased from OLS (R² = 0.7767) to GWR (R² = 0.9225) and MGWR (R² = 0.9296). MGWR bandwidths were localized for Functional Density (61), POI Diversity (62), and Mean NDVI (71), but broader for Transit Proximity (2548) and Commercial Proximity (2619). Adjusted local inference identified significant positive associations for Functional Density, POI Diversity, and Transit Proximity, significant negative associations for Mean NDVI, and no significant local association for Commercial Proximity. Although sensitivity analyses generally preserved fit and median directions, excluding building height changed the Transit Proximity bandwidth from 2548 to 70 neighbors, indicating that its estimated association scale was sensitive to vitality-index specification. Commercial Proximity also remained sensitive to indicator operationalization. The findings identify where local diagnostic follow-up is warranted, while intervention effects remain outside the scope of the analysis. Full article

Nighttime light (NTL) data is widely used to monitor urban development, economic activity and population distribution, supporting socio-economic analysis and sustainable development planning. SDGSAT-1 NTL data, with its multiple bands and high resolution, has become an important source for studying human activity patterns. However, directly summing radiance values on geographic grids assumes equal contributions from all grid cells, regardless of the differences in the ground area represented by each cell. As a result, regional nighttime light totals may be systematically distorted, particularly in cross-latitude analyses where pixel areas differ substantially. To address this aggregation issue, this study proposes a physically explicit area-weighting framework that converts SDGSAT-1 radiance to radiant flux by incorporating the actual surface area represented by each grid cell. The framework is validated using 25 cities spanning different latitudes and development levels in the Northern Hemisphere. Results show that the radiant flux model, which captures total emitted power, demonstrates improved performance over radiance-based aggregation (a density-based measure) in population estimation (R² = 0.85 vs. 0.82). By shifting from light intensity to integrated total energy, this approach improves cross-latitude comparability and enhances the methodological robustness of NTL-based analyses. Full article

With the increase in global fishing activities, the transparency of fishery data has drawn increasing attention. Data transparency enables stakeholders to play a greater role in ensuring that fisheries are legal, ethical, and sustainable. Improving the transparency of fishing vessel data can effectively support vessel monitoring and enhance fishery safety. This paper presents a systematic review of satellite remote sensing modalities and datasets currently available for fishing vessel identification and monitoring. Conducted in accordance with the PRISMA 2020 guidelines, this study employs a dual-track search strategy to retrieve, screen, and synthesize academic literature and public datasets from mainstream databases, including the Web of Science Core Collection, IEEE Xplore, and CNKI. First, the existing remote sensing modalities were classified into three major categories based on their imaging principles: synthetic aperture radar (SAR), optical remote sensing, and nighttime light (NTL) remote sensing. In addition, the mainstream satellite data sources and their corresponding parameters were summarized for each category. Second, an in-depth comparative analysis of these remote sensing modalities is conducted from core dimensions such as target detection sensitivity, robustness under complex environments and meteorological conditions, and spatiotemporal resolution. This reveals the performance limitations and significant complementarity of different sensor data in fishing vessel detection. Finally, mainstream remote sensing datasets for fishing vessels (such as xView3-SAR, xView, and VBD) are summarized and evaluated, pointing out the gaps in certain types of datasets. In conclusion, this paper suggests that building a “full spatiotemporal and multi-scale” observation framework based on multi-source heterogeneous data fusion is an important trend for the future development of fishing vessel detection using remote sensing, aiming to provide a reference for relevant researchers. Full article

Urban green spaces are an integral part of urban ecosystems and play a crucial role in improving the urban ecological environment and promoting sustainable urban development. This study aimed to quantify the spatiotemporal evolution of urban green spaces in central Shenyang and to assess how their relationships with selected urban development and socioeconomic drivers varied across space and time. Urban green space information for 2010, 2015, and 2020 was extracted from remote sensing imagery using ENVI and ArcGIS. Eight indicators—impervious surface intensity, population density, nighttime light intensity, economic density, industrial structure, road density, traffic accessibility, and land use intensity—were examined using spatial-pattern and contribution analyses, ordinary least squares regression (OLS), and geographically weighted regression (GWR). Urban green space areas were 330.56 km² (23.55%), 344.55 km² (24.55%), and 337.32 km² (24.03%) in 2010, 2015, and 2020, respectively, representing a net increase of 6.76 km² (2.05%) and an annual dynamic degree of 0.20% over the study period. Spatially, green spaces were concentrated in peripheral areas, fragmented in the central area, and prominently distributed along river corridors. At α = 0.05, coefficient-level OLS results showed negative associations with impervious surface intensity in 2010 (standardized β = −0.184, p < 0.001) and 2015 (β = −0.098, p = 0.005). In 2020, impervious surface intensity showed a small positive coefficient (β = 0.056, p = 0.043), whereas traffic accessibility showed a negative coefficient (β = −0.0556, p = 0.033). The OLS models had low explanatory power, with R² values of 0.034, 0.008, and 0.007 and adjusted R² values of 0.029, 0.003, and 0.002 for 2010, 2015, and 2020, respectively. The GWR local R² values ranged from 0.000 to 0.310 and showed spatially shifting high-value areas, indicating marked spatial variation in model explanatory power. These findings provide case-specific evidence for Shenyang and a transferable analytical framework for spatially differentiated green space planning in comparable old industrial and rapidly restructuring cities. Full article

Ecological security pattern (ESP) planning typically emphasizes ecological structure and connectivity, but may provide limited information on long-term functional degradation occurring within ecologically important areas. To address this gap, this study develops a baseline-referenced framework for diagnosing ecological resilience degradation (ERD) by integrating ecosystem service value (ESV) dynamics, minimum cumulative resistance (MCR) modeling, and explainable machine learning (XGBoost–SHAP). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, ERD was operationally defined as long-term functional degradation within ecologically important components of the 2000 baseline network, with net ESV decline from 2000 to 2020 used as the functional-degradation signal. Two complementary ERD patterns were distinguished: source erosion and corridor interruption. The results showed that ERD exhibited a pronounced core–periphery pattern, with higher degradation intensity concentrated along the Guangzhou–Foshan–Dongguan–Shenzhen urban development corridor. Integrated ERD covered 5452.69 km², of which source erosion accounted for 4238.32 km² (77.73%) and corridor interruption for 1214.37 km² (22.27%). Source erosion represented the dominant ERD pattern in terms of spatial extent. The XGBoost model showed moderate predictive performance under spatial block cross-validation (mean validation R² = 0.638 ± 0.043), and SHAP analysis indicated that vegetation condition, proximity to water bodies, nighttime light, and elevation were among the most influential factors associated with spatial variation in ERD intensity. NDVI contributions shifted from positive to negative around 0.65, while the distance-to-water response changed most rapidly within 200–300 m. These values are interpreted as empirical transition ranges in the model response. Overall, the proposed framework links long-term ecosystem functional degradation with baseline ecological network position and provides a spatially explicit basis for identifying functionally degraded ecological areas and supporting differentiated spatial prioritization and ecological management. Full article

Infrared and visible image fusion combines the thermal cues captured by infrared sensors with the rich structural and texture information provided by visible images. This technique is particularly valuable for security surveillance, nighttime scene perception, and target recognition under challenging environmental conditions. Existing methods generally adopt fixed fusion strategies and neglect dynamic dual-modality changes under low illumination, overexposure and strong light interference, failing to preserve both thermal target saliency and visible structural details in fused images. To address this problem, this paper proposes a Lighting-Aware Spatial–Frequency Fusion Network (LASFNet) for security surveillance. It first estimates modality reliability across low-light, overexposed and infrared-salient regions, incorporating it into the fusion of frequency-domain amplitude and phase. Spatial infrared, visible and frequency-domain compensation features are then jointly fused to generate the output. Experiments on M3FD, MSRS and RoadScene datasets show that LASFNet achieves competitive fusion performance with strong cross-dataset generalization. On M3FD, YOLOv8s with LASFNet-fused inputs achieves 84.825% mAP@0.5 and 57.319% mAP@0.5:0.95, outperforming visible, infrared and YDTR-fused inputs. The proposed method balances target saliency and scene structure, providing more effective visual input for object detection under complex illumination. Full article

Nighttime driving safety remains a critical challenge in modern transportation systems: insufficient ambient lighting significantly degrades visual perception quality, adversely affecting both human drivers and advanced driver-assistance systems (ADAS) and directly threatening road users’ safety. Traditional image enhancement methods often suffer from color distortion and visual artifacts, whereas existing deep learning approaches typically require paired training data and incur substantial computational overhead. To address these limitations, this paper presents BLEN (bio-inspired low-light enhancement network), a zero-reference deep learning framework that integrates biological vision principles with efficient convolutional architectures. Specifically, BLEN leverages Retinex theory for illumination–reflectance decomposition, is inspired by and functionally approximates lateral inhibition mechanisms for edge enhancement, and incorporates a Large-Kernel Convolution with Attention (LKCA) module that reduces the parameter count of the LKCA encoder block by 76% (0.56 M vs. 2.34 M for a standard 13 × 13 convolution) relative to standard large-kernel operations. Extensive experiments on the SICE and LOL benchmarks demonstrate that BLEN achieves state-of-the-art performance among real-time, edge-deployable zero-reference methods on the SICE benchmark, yielding a peak signal-to-noise ratio (PSNR) of 23.67 ± 0.14 dB and a structural similarity index measure (SSIM) of 0.891 ± 0.004 on SICE while maintaining 2.10 M parameters (2.1 MB in INT8, 8.4 MB in FP32). Furthermore, the proposed method enables real-time inference at 31 frames per second (FPS) on embedded platforms, including the HiSilicon SS928 and Jetson Nano, demonstrating that the proposed method is an efficient and effective front-end for camera-based ADAS perception on automotive-grade edge hardware. Full article

Spectral unmixing of outdoor nighttime lighting mixtures is a linear but challenging problem due to spectral variability and contributions from unknown light sources. This paper presents a quadratic programming algorithm for constrained linear unmixing abundance estimation that extends the conventional fully constrained least squares approach through a physically motivated set of three linear constraints: estimated abundances must be non-negative, their sum must not be greater than unity, and the spectral signature reconstructed from estimated abundances must not exceed the measured signal in any spectral band. Unlike alternative optimization or neural network approaches, the proposed formulation requires no hyperparameter tuning or stochastic training, and yields a unique globally optimal solution whenever the endmember spectra are linearly independent, as verified for the case study considered here. The accuracy of the algorithm is evaluated using a physically motivated dataset generation framework grounded in the Generalized Linear Mixing Model, which is capable of modeling wavelength-dependent surface reflections and variable fractions of unknown light sources. Experiments conducted on a database of measured illumination sources demonstrate that the proposed formulation outperforms the classical fully constrained least squares baseline. Specifically, it reduces the abundance RMSE from 0.1539 to 0.1484 and improves the endmember detection F1 score from 0.795 to 0.803, with differences confirmed to be statistically significant. Furthermore, the model remains robust to surface reflections present in up to 80% of the evaluated mixtures. It maintains reliable abundance estimates for unknown spectral contributions up to approximately 15%, with accuracy gradually decreasing beyond this level. Finally, both the optimization method and the dataset generation framework are completely independent of the underlying channel configuration, making them readily adaptable to arbitrary choices of spectral channels and application domains beyond public lighting analysis. Full article

Background: Public AIS data may represent light-attracted fisheries unevenly across coastal regions, whereas VIIRS–DNB provides a complementary optical observation of bright offshore activity. Aims: We develop a regional framework to compare VIIRS-derived fishing-light patterns with AIS-derived vessel activity and to compare model-inferred environmental associations between East Asian and Southeast Asian coastal waters. Methods: Monthly VIIRS–DNB composites, GFW AIS-derived fishing effort and gear categories, and monthly CMEMS sea-surface temperature (SST), sea-surface height (SSH), sea-surface salinity (SSS), surface-current velocities, dissolved oxygen (DO), mixed-layer depth (MLD), and chlorophyll-a (Chl-a) were harmonized for 2017–2020. VIIRS detections were screened for coastal and fixed-light contamination, and spatial density, temporal correlation, spatial-error model (SEM), Geodetector, and MaxEnt analyses were applied. Results: East Asia showed more continuous high-intensity fishing-light belts with summer–autumn peaks, whereas Southeast Asia showed more fragmented activity with a stronger spring peak. In Southeast Asia, squid-jigging activity had the closest spatial correspondence among AIS categories and strong temporal synchronization with fishing-light intensity (Spearman’s $r_s$ = 0.890, p < 0.001), but its spatial explanatory power remained limited (q = 7.8%, p < 0.001). Within the selected predictor set, SST had the highest model-derived contribution in East Asia, whereas SSS had the highest model-derived contribution in Southeast Asia; these results represent model-inferred environmental associations rather than causal effects. Conclusions and recommendations: Regional contrasts describe VIIRS-derived fishing-light patterns, whereas differences in AIS correspondence indicate potential observation gaps but may also reflect fleet composition, vessel-light intensity, and genuine differences in fishing activity. We recommend combining optical and tracking observations and reporting uncertainty from AIS coverage, VIIRS detection limits, spatial sampling, and environmental-model assumptions. Full article