Search Results (120)

This study evaluated the performance of the operational GK2A (GEO-KOMPSAT-2A) fog detection algorithm (GK2A_FDA) using ground-based visibility meter data from 176 stations across South Korea from 2021 to 2023. According to the verification method using the nearest pixel and 3 × 3 neighborhood pixel approaches to the visibility meter, the 3-year average probability of detection (POD) is 0.59 and 0.70, the false alarm ratio (FAR) is 0.86 and 0.81, and the bias is 4.25 and 3.73, respectively. POD is highest during daytime (0.72; bias: 7.34), decreases at night (0.57; bias: 3.89), and is lowest at twilight (0.52; bias: 2.36). The seasonal mean POD is 0.65 in winter, 0.61 in spring and autumn, and 0.47 in summer, with August reaching the minimum value, 0.33. While POD is higher in coastal areas than inland areas, inland regions show lower FAR, indicating more stable performance. Over-detections occurred regardless of geographic location and time, mainly due to the misclassification of low-level clouds and cloud edges as fog. Especially after sunrise, the fog dissipated and transformed into low-level clouds. These findings suggest that there are limitations to improving fog detection levels using satellite data alone, especially when the surface is obscured by clouds, indicating the need to utilize other data sources, such as objective ground-based analysis data. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

The Ice, Cloud, and land Elevation Satellite-2 has provided unprecedented global surface elevation measurements through photon-counting Lidar (Light detection and ranging), yet its low signal-to-noise ratio (SNR) poses significant challenges for denoising algorithms. Existing methods, relying on fixed parameters, struggle to adapt to dynamic noise distribution in rugged mountain regions where signal and noise change rapidly. This study proposes an adaptive Bayesian denoising algorithm integrating minimum spanning tree (MST) -based slope estimation and probabilistic parameter optimization. First, a simulation framework based on ATL03 data generates point clouds with ground truth labels under varying SNRs, achieving correlation coefficients > 0.9 between simulated and measured distributions. The algorithm then extracts surface profiles via MST and coarse filtering, fits slopes with >0.9 correlation to reference data, and derives the probability distribution function (PDF) of neighborhood photon counts. Bayesian estimation dynamically selects optimal clustering parameters (search radius and threshold), achieving F-scores > 0.9 even at extremely low SNR (1 photon/10 MHz noise). Validation against three benchmark algorithms (OPTICS, quadtree, DRAGANN) on simulated and ATL03 datasets demonstrates superior performance in mountainous terrain, with precision and recall improvements of 10–20% under high noise conditions. This work provides a robust framework for adaptive parameter selection in low-SNR photon-counting Lidar applications. Full article

Whale signals originating in the vicinity of a triplet of underwater hydrophones, at a 2 km distance from each other, are recorded at the three sensors. They offer the opportunity to test simple models of propagation applied in the immediate neighborhood of the triplet, by comparing the arrival times and amplitudes of direct and reflected paths between the whale and the three hydrophones. Examples of recordings of individual fin whales passing by hydrophone triplets, based on the characteristics of their vocalizations around 20 Hz, are presented. Two types of calls are observed and their source wavelets extracted. Time segments are delimited around each call using a power detector. The time of arrival of the direct wave to the sensor and the Time Differences of Arrivals (TDOA) between sensors are obtained by correlation of the extracted source wavelets within the time segments. In addition to direct arrival, multiple reflections and the delays between the reflection and the direct arrival are automatically picked. A grid-search method of tracking the calls is presented based on the TDOA between three hydrophones and reflection delay times. Estimates of the depth of vocalization of the whale are made assuming a simple straight ray propagation model. The amplitude ratios between two hydrophones follow the spherical amplitude decay law of one over distance when the cetacean is in the immediate vicinity of the triplet, in a circle of radius 1.5 km sharing its center with the triplet’s center. Full article

Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality. Full article

Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often suffer from low robustness and poor generalization. In response, this paper presents a novel line feature matching approach grounded in point–line invariants through spatial invariant relationships. By leveraging a robust point feature matching algorithm, an initial set of point feature matches is acquired. Subsequently, the line feature supporting area is partitioned, and a constant ratio invariant is formulated based on the distances from point to line features within corresponding neighborhood domains. Additionally, a direction vector invariant is also introduced, jointly constructing a dual invariant for line matching. An initial matching matrix and line feature match pairs are derived using this dual invariant. Subsequent geometric constraints within line feature matches eliminate residual outliers. Comprehensive evaluations under diverse imaging conditions, along with comparisons to several state-of-the-art algorithms, demonstrate that our proposal achieved remarkable performance in terms of both accuracy and robustness. Our implementation code will be publicly released upon the acceptance of this paper. Full article

Currently, urban development has entered the stage of renewal and transformation. Energy transition is an important trend for sustainable urban development, and the assessment of solar energy potential in old residential areas in cold regions is of great significance. This study selects 47 old residential communities in Jinan, a cold region of China, as case samples. Using clustering algorithms based on spatial form characteristic parameters, the study divides the samples into five categories. The study then uses the Ladybug tool to simulate the distribution and total solar energy utilization potential of buildings in the five categories and analyzes the correlation between eight spatial form parameters and building solar energy potential. A linear regression model is established, and strategies for the application of BIPV in community buildings are proposed. The study finds that factors such as plot ratio, building density, open space ratio, volume-to-surface ratio, and form coefficient have a significant impact on the solar energy potential of residential communities; the p-values are −0.785, −0.783, 0.783, −0.761, and 0.724, respectively. Among these, building density (BD) is the most crucial factor affecting the solar energy potential of building facades. Increasing by one unit can reduce the solar energy utilization potential by 28.00 kWh/m²/y. At the same time, installing photovoltaic panels on old residential buildings in cold regions can reduce building carbon emissions by approximately 48%. The research findings not only provide methodological references for photovoltaic technology application at varying neighborhood scales in urban settings but also offer specific guidance for low-carbon retrofitting of aging urban communities, thereby facilitating progress in urban carbon emission reduction. Full article

Accurate mapping of rice planting areas is of great significance in terms of food security and market stability. However, the existing research into high-resolution rice mapping has relied heavily on fine-scale temporal remote sensing image data. Due to cloud occlusion and banding problems, data extraction from Landsat series remote sensing images with medium spatial resolution is not optimal. Therefore, this study proposes a rice mapping method (LR) using Google Earth Engine (GEE), which uses Landsat images and integrates automatic generation of training samples and a machine learning algorithm, with the assistance of phenological methods. The proposed LR method initially generated rice distribution maps based on phenology, and 300 sample points were selected for meta-identification of rice images via an enhanced pixel-based phenological feature composite method (Eppf-CM) utilizing high-resolution imagery. Subsequently, the inundation frequency (F) and an improved sample point statistical feature, i.e., the ratio of change amplitude of LSWI to NDVI (RCLN), were introduced to combine Eppf-CM with combined consideration of vegetation phenology and surface water variation (CCVS) methods, to automate the generation of training data with the aid of phenology. The sample data were optimized by an alternate iterative method involving extraction of neighborhood information. Finally, a random forest (RF) probabilistic model trained by integrating data from different phenological periods was used for rice mapping. To test its performance, we mapped rice distribution at 30 m resolution (“LR_Rice”) across Heilongjiang Province, China from 2010 to 2022, with annual overall accuracy (OA) and Kappa coefficients greater than 0.97 and 0.95, respectively, and compared them with four existing rice mapping products. The spatial distribution characteristics of rice cultivation extracted by the LR algorithm were accurate and the performance was optimal. In addition, the extracted area of LR_Rice was highly consistent with the agricultural statistical area; the coefficient of determination R² was 0.9915, and the RMSE was 22.5 kha. The results show that this method can accurately obtain large-scale rice planting information, which is of great significance for food security, water resource management, and environmentally sustainable development. Full article

With the launch of the ICESat-2 satellite, global-scale forest parameter monitoring has entered a new phase. However, the background noise in ICESat-2 lidar data significantly impairs the accuracy of signal photon extraction. This study introduces a direction-adaptive DBSCAN method for denoising ICESat-2 photon point clouds, integrating elevation histogram-based coarse denoising with adaptive clustering for fine denoising. The method is applied to data from the Gongbella River Nature Reserve. An innovative aspect of this approach is the introduction of elliptical tilt angle adaptation, which dynamically adjusts the elliptical orientation of the photon point cloud to determine the optimal tilt angle, thus optimizing the denoising effect and reducing computational and memory demands. The direction-adaptive DBSCAN algorithm improves denoising accuracy by dynamically adjusting the neighborhood radius based on the elliptic tilt angle and the distribution of the point cloud. Additionally, the density threshold selection is optimized using the Otsu method, enhancing the accuracy of distinguishing noise photons from signal photons. The method was validated using data from the Gongbella River Nature Reserve, showing significant improvements in denoising accuracy. Compared to existing methods, recall (R) increased by 6.91%, precision (P) improved by 8.82%, and both the F1-score and accuracy rose by 9.52%. The photon point cloud denoising algorithm demonstrated substantial accuracy improvements across multiple data strips, making it particularly effective for processing complex data from ICESat-2, with broad application potential. Both quantitative and qualitative analyses confirm that the algorithm outperforms traditional methods in signal-to-noise ratio and denoising performance, providing reliable technical support for extracting photon point cloud elevation data from forest surfaces and canopies. Full article

Aiming to address the fundamental limitation of fixed activation functions that constrain network expressiveness in existing deep deblurring models, in this pioneering study, we introduced Kolmogorov–Arnold Networks (KANs) into the field of full-color/RGB image deblurring, proposing the Kolmogorov–Arnold and Transformer Network (KT-Deblur) framework based on dynamically learnable activation functions. This framework overcomes the constraints of traditional networks’ fixed nonlinear transformations by employing adaptive activation regulation for different blur types through KANs’ differentiable basis functions. Integrated with a U-Net architecture within a generative adversarial network framework, it significantly enhances detail restoration capabilities in complex scenarios. The innovatively designed Unified Attention Feature Extraction (UAFE) module combines neighborhood self-attention with linear self-attention mechanisms, achieving synergistic optimization of noise suppression and detail enhancement through adaptive feature space weighting. Supported by the Fast Spatial Feature Module (FSFM), it effectively improves the model’s ability to handle complex blur patterns. Our experimental results demonstrate that the proposed method outperforms existing algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics across multiple standard datasets, achieving an average PSNR of 41.25 dB on the RealBlur-R dataset, surpassing the latest state-of-the-art (SOTA) algorithms. This model exhibits strong robustness, providing a new paradigm for image-deblurring network design. Full article

A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a “morphology generation–performance evaluation–morphology optimization” framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a “morphology–performance” dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor–area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume–Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization. Full article

Geiger-mode avalanche photodiode (GM-APD) is a single-photon-detection device characterized by high sensitivity and fast response, which enables it to detect echo signals of distant targets effectively. Given that weak and small targets possess relatively small volumes and occupy only a small number of pixels, relying solely on neighborhood information for target reconstruction proves to be difficult. Furthermore, during long-distance detection, the optical reflection cross-section is small, making signal photons highly susceptible to being submerged by noise. In this paper, a noise fitting and removal algorithm (NFRA) is proposed. This algorithm can detect the position of the echo signal from the photon statistical histogram submerged by noise and facilitate the reconstruction of weak and small targets. To evaluate the NFRA method, this paper establishes an optical detection system for remotely detecting active single-photon weak and small targets based on GM-APD. Taking unmanned aerial vehicles (UAVs) as weak and small targets for detection, this paper compares the target reconstruction effects of the peak-value method and the neighborhood method. It is thereby verified that under the conditions of a 7 km distance and a signal-to-background ratio (SBR) of 0.0044, the NFRA method can effectively detect the weak echo signal of the UAV. Full article

In modern medicine, studies devoted to the assessment of the parameters of residential infrastructure and the population’s attitude towards them have become quite large-scale. Objectives: The aim of the study was to establish associations between individually perceived parameters of residential infrastructure and the main modifiable cardiovascular risk factors (hypertension, obesity, lipid and carbohydrate metabolism disorders) in one of the subjects of the Russian Federation. Methods: The epidemiological study “Study of the influence of social factors on chronic non-communicable diseases” started in 2015 and ended in 2023. The sample was formed by using the stratification method based on the assignment to a medical organization. The study included 1598 respondents aged 35 to 70 years (491 rural residents). The study of infrastructure parameters was conducted based on the subjective opinions of respondents using the neighborhood environment walkability scale (NEWS) questionnaire, divided into eight scales. Logistic regression analysis was used to identify associations between infrastructure parameters and cardiovascular risk factors; the odds ratio (OR) and 95% confidence interval were evaluated. Results: Individually perceived infrastructure parameters of the scale B, reflecting the accessibility of infrastructure facilities, were associated with hypertension [OR = 1.33], obesity [OR = 1.40], and abdominal obesity [OR = 1.59]. Elements of the social infrastructure of the scale C, describing the streets in the residential area, increased the likelihood of developing obesity [OR = 1.42] and visceral obesity [OR = 1.43]. The characteristics of the residential area, represented by the scale D that evaluates pedestrian infrastructure, were associated with all major cardiovascular risk factors (hypertension [OR = 1.65], obesity [OR = 1.62] and abdominal obesity [OR = 1.82], and disorders of lipid [OR = 1.41] and carbohydrate metabolism [OR = 1.44]). Conclusion: Social factors represented by various aspects of infrastructure have become important criteria for determining cardiovascular health. Environmental conditions affect cardiovascular risk factors through behavioral patterns that shape the respondent’s lifestyle. Interventions in urban planning—increasing accessibility to infrastructure facilities for the population, developing a pedestrian-friendly urban environment, improving physical activity resources in areas, planning recreation areas, and landscaping—can become the most important concept for the prevention of cardiovascular diseases. Full article

The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops. Full article

Hyperuricemia, a major worldwide burden on public hygiene, is closely connected with dietary habits. However, few studies have evaluated the association of dietary diversity with hyperuricemia. To preliminarily reveal the status of a diversified diet in preventing hyperuricemia based on a neighborhood-based, massive-scale cohort in China, a total of 43,493 participants aged 20–74 years old, with no history of hyperuricemia at baseline, were enrolled in the research from April 2016 to December 2019. The Dietary Diversity Score (DDS) was utilized to evaluate the dietary variety and split the participants into the low-, medium-, and high-DDS groups. Information on participants was connected to regional health information systems that acquired data on hyperuricemia instances up to 28 February 2023. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed by Cox proportional hazards models. Restricted cubic splines (RCS) were implemented to analyze dose–response correlation. A total of 1460 individuals with newly diagnosed hyperuricemia were observed over a median follow-up period of 5.59 years. Compared to the low-DDS group, HRs for the medium- and high-DDS groups were 0.87 (95% CI 0.76–0.99) and 0.80 (95% CI 0.70–0.91) in the fully adjusted model, respectively. The risk of hyperuricemia incidence was reduced by 5% for each 1 unit of DDS increase. A linear correlation of DDS with hyperuricemia emerged and further revealed that the intake of 8–10 broad categories of food could decrease the incidence of hyperuricemia. Our results validate the dietary principle of “food diversification” recommended in guidelines. Conclusions should be applied with caution considering the paucity of related evidence in additional nations. Full article