Search Results (441)

Single image super-resolution is the inverse problem of reconstructing a high-resolution image from its low-resolution counterpart. Although recent Transformer-based architectures leverage global context integration to improve reconstruction quality, they often overlook frequency-specific characteristics, resulting in the loss of high-frequency information. To address this limitation, we propose the Dual Frequency Adaptive Network (DFAN). DFAN first decomposes the input into low- and high-frequency components via Stationary Wavelet Transform. In the low-frequency branch, Swin Transformer layers restore global structures and color consistency. In contrast, the high-frequency branch features a dedicated module that combines Directional Convolution with Residual Dense Blocks, precisely reinforcing edges and textures. A frequency fusion module then adaptively merges these complementary features using depthwise and pointwise convolutions, achieving a balanced reconstruction. During training, we introduce a frequency-aware multi-term loss alongside the standard pixel-wise loss to explicitly encourage high-frequency preservation. Extensive experiments on the Set5, Set14, BSD100, Urban100, and Manga109 benchmarks show that DFAN achieves up to +0.64 dBpeak signal-to-noise ratio, +0.01 structural similarity index measure, and −0.01learned perceptual image patch similarity over the strongest frequency-domain baselines, while also delivering visibly sharper textures and cleaner edges. By unifying spatial and frequency-domain advantages, DFAN effectively mitigates high-frequency degradation and enhances SISR performance. Full article

The context of global climate change, water stress has a significant impact on the ecological function and landscape value of urban greening shrubs. In this study, three typical greening shrubs (Euonymus japonicus, Ligustrum × vicaryi, and Berberis thunbergii var. atropurpurea) in North China were subjected to a two-year field-controlled experiment (2022–2023) with four water treatments: full irrigation, deficit irrigation, natural rainfall, and extreme drought. The key findings are as follows: (1) Extreme drought reduced the color indices substantially—the G_CC of E. japonicus decreased by 40% (2023); the R_CC of B. thunbergii var. atropurpurea declined by 35% (2022); and the color indices of L. × vicaryi remained stable (variation < 15%). (2) Early-season soil water content (SWC) strongly correlated with the color index of E. japonicus (r² = 0.42, p < 0.05) but weakly with B. thunbergii (r² = 0.28), suggesting species-specific drought-tolerance mechanisms like reduced leaf area. (3) Deficit irrigation (SWC ≈ 40%) maintained color indices between fully irrigated and drought-stressed levels. Notably, B. thunbergii retained high redness (R_CC > 0.8) at an SWC ≈ 40%; E. japonicus required an SWC > 60% to preserve greenness (G_CC). The research results provide a scientific basis for urban greening plant screening and water-saving irrigation strategies, and expand the application scenarios of color coordinates in plant physiological and ecological research. Full article

During a volcanic eruption, a large volume of pyroclastic material can be deposited on the roads and roofs of the urban areas near volcanoes. The use of volcanic ash as an additive for the manufacture of bricks provides a solution to the disposal of part of this natural residue and reduces the depletion of a non-renewable natural resource, clayey soil, which brings some environmental and economic advantages. The pore system, compactness, uniaxial compression strength, thermal conductivity, color and durability of bricks without and with the addition of volcanic ash were evaluated through hydric tests, mercury intrusion porosimetry, ultrasound, uniaxial compression tests, IR thermography, spectrophotometry and salt crystallization tests. The purpose of this research is to determine the feasibility of adding 10, 20 and 30% by weight of volcanic ash from La Palma (Canary Islands, Spain) in two grain sizes to produce bricks fired at 800, 950 and 1100 °C. The novelty of this study is to use two sizes of volcanic ash and fire the samples at 1100 °C, which is close to the liquidus temperature of basaltic magmas and allows a high degree of interaction between the volcanic ash and the brick matrix. The addition of fine volcanic ash was found to decrease the porosity of the bricks, although the use of high percentages of coarse volcanic ash resulted in bricks with almost the same porosity as the control samples. The volcanic ash acted as a filler, reducing the number of small pores in the bricks. The presence of vesicles in the volcanic ash reduced the compressive strength and the compactness of the bricks with additives. This reduction was more evident in bricks manufactured with 30% of coarse volcanic ash and fired at 800 and 950 °C, although they still reached the minimum resistance required for their use in construction. No significant differences in thermal conductivity were noticed between the bricks with and without volcanic ash additives, which is crucial in terms of energy savings and the construction of sustainable buildings. At 1100 °C the volcanic ash changed in color from black to red. As a result, the additive blended in better with the matrix of bricks fired at 1100 °C than in those fired at 800 and 950 °C. The bricks with and without volcanic ash and fired at 1100 °C remained intact after the salt crystallization tests. Less salt crystallized in the bricks with volcanic ash and fired at 800 and 950 °C than in the samples without additives, although their low compressive strength made them susceptible to decay. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model’s ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyperparameter studies indicate that the model attains optimal performance with a learning rate of 2 × 10⁻⁴, a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

Ailanthus altissima (AA) is an invasive tree species rapidly spreading worldwide, colonizing both urban and agricultural or forestry environments. This three-year study aimed to assess its effects on the growth and yield traits of the Koroneiki olive cultivar under co-cultivation in pots, combined with two irrigation regimes, full and deficit irrigation (60% of full). Within each irrigation regime, olive trees were grown either in the presence or absence (control) of AA. The trial evaluated several parameters, including vegetative growth, yield traits, and oil quality characteristics. Co-cultivation with AA had no significant impact on tree growth after three years, though it significantly reduced oil content per fruit. Antioxidant capacity of the oil improved under deficit irrigation, while AA presence did not significantly affect it, except for an increase in o-diphenol concentration. Neither the fatty acid profile nor squalene levels were significantly influenced by either treatment. Fruit weight and color were primarily affected by deficit irrigation. During storage, olive oil quality declined significantly, with pre-harvest treatments (presence or absence of AA and full or deficit irrigation regime) playing a critical role in modulating several quality parameters. In conclusion, the presence of AA near olive trees did not substantially affect the key quality indices of the olive oil, which remained within the criteria for classification as extra virgin. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

The challenges of globalization and urbanization increasingly impact the Historic Urban Landscape (HUL), yet fine-grained and quantitative methods for evaluating HUL remain limited. Adopting a human-centered perspective, this study introduces a novel framework to quantitatively evaluate HUL through the lens of Historical Appearance Integrity (HAI). An evaluation system comprising four key dimensions (building materials, building colors, decorative details, and streetscape morphology) was constructed using the Analytic Hierarchy Process (AHP). An Elo rating system was subsequently applied to quantify the scores of the indicators. A prediction model was developed based on transfer learning and feature fusion to estimate the scores of the indicators. The model achieved accuracies above 93% and loss values below 0.2 for all four indicators. The framework was applied to the Inner Qinhuai Historical Character Area in Nanjing for validation. Results show that the spatial distribution of HAI in the area exhibits significant spatial heterogeneity. On a 0–100 scale, the average HAI scores were 23.17 for primary roads, 27.73 for secondary roads, and 46.93 for branch roads. This study offers a fine-grained, automated approach to evaluate HAI along urban streets and provides a quantitative reference for heritage conservation and urban renewal strategies. Full article

Understanding how people perceive urban streetscapes is essential for enhancing the visual quality of the urban environment and optimizing street space design. While perceptions are shaped by the interplay of multiple visual elements, existing studies often isolate single semantic features, overlooking their combinations. This study proposes a Landscape Element Combination Extraction Method (SLECEM), which integrates the UniSal saliency detection model and semantic segmentation to identify landscape combinations that play a dominant role in human perceptions of streetscapes. Using street view images (SVIs) from the central area of Futian District, Shenzhen, China, we further construct a multi-dimensional feature–perception coupling analysis framework. The key findings are as follows: 1. Both low-level visual features (e.g., color, contrast, fractal dimension) and high-level semantic features (e.g., tree, sky, and building proportions) significantly influence streetscape perceptions, with strong nonlinear effects from the latter. 2. K-Means clustering of salient landscape element combinations reveals six distinct streetscape types and perception patterns. 3. Combinations of landscape features better reflect holistic human perception than single variables. 4. Tailored urban design strategies are proposed for different streetscape perception goals (e.g., beauty, safety, and liveliness). Overall, this study deepens the understanding of streetscape perception mechanisms and proposes a highly operational quantitative framework, offering systematic theoretical guidance and methodological tools to enhance the responsiveness and sustainability of urban streetscapes. Full article

In addition to architecture and infrastructure, urban outdoor advertising also shapes urban visual identity, serving as a prominent carrier of public information and visual stimuli. However, excessive or poorly designed advertisements disrupt the cityscape and contribute to visual pollution and cognitive overload. Leveraging computer-based eye tracking, this study examines the visual and cognitive effects of outdoor advertising designs within urban contexts. Key eye-tracking metrics, including total fixation duration, fixation count, time to first fixation, and first fixation duration, are measured to analyze the influence of various variables on visual attention and user experience, such as color contrast, text complexity, information hierarchy, and spatial layout. The findings reveal that high-contrast, text-heavy designs hinder visual flow and increase mental effort, while visually balanced layouts improve legibility and reduce cognitive burden. These results offer actionable insights for optimizing urban visual identity and enhancing the clarity, comfort, and coherence of outdoor advertising. By integrating perceptual data into urban design strategies, this research provides a data-driven approach to smarter, more human-centered advertising management and urban aesthetic governance. Full article

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element. Full article

Urban color represents the visual skin of a city, embodying regional culture, historical memory, and the contemporary spirit. However, while the existing studies focus on pedestrian-level facade colors, the “fifth facade” from a bird’s-eye view has been largely overlooked. Moreover, color distortions in traditional remote sensing imagery hinder precise analysis. This study targeted 56 Chinese coastal cities, decoding the spatiotemporal patterns of their fifth facade color (FFC). Through developing an innovative natural color optimization algorithm, the oversaturation and color bias of Sentinel-2 imageries were addressed. Several color indicators, including dominant colors, hue–saturation–value, color richness, and color harmony, were developed to analyze the spatial variations of FFC. Results revealed that FFC in Chinese coastal cities is dominated by gray, black, and brown, reflecting the commonality of cement jungles. Among them, northern warm grays exude solidity, as in Weifang, while southern cool grays convey modern elegance, as in Shenzhen. Blue PVC rooftops (e.g., Tianjin) and red-brick villages (e.g., Quanzhou) serve as symbols of industrial function and cultural heritage. Economically advanced cities (e.g., Shanghai) lead in color richness, linking vitality to visual diversity, while high-harmony cities (e.g., Lianyungang) foster livability through coordinated colors. The study also warns of color pollution risks. Cities like Qingdao exposed planning imbalances through color clashes. This research pioneers a systematic and large-scale decoding of urban fifth facade color from a remote sensing perspective, quantitatively revealing the dilemma of “identical cities” in modernization development. The findings inject color rationality into urban planning and create readable and warm city images. Full article

We explored anodic aluminum oxide (AAO) stealth materials combining low infrared emissivity and visible structural coloration through multi-parameter modulation. Using DC ion gold sputtering and UHV magnetron chromium sputtering, we successfully prepared an AAO stealth material with high-saturation visible structural coloration and low infrared emissivity (ε < 0.17). Quantitative evaluation based on the CIE Lab color difference model indicated that the gold-coated samples had high matching accuracy with PANTONE standard colors ($\mathsf{\Delta }{E}_{ab}^{*}$ < 1.6). The chromium-coated samples had slightly lower matching accuracy ($\mathsf{\Delta }{E}_{ab}^{*}$ < 3.0), but still displayed rich coloration, with color difference within human-perceptible tolerance limits. Full article

Leachates generated from the degradation of green waste and biosolids from urban wastewater treatment plants (WWTPs) pose significant environmental concerns due to high concentrations of organic pollutants and heavy metals. This study proposes a hybrid treatment strategy combining electrocoagulation (EC) and UVC-activated TiO₂ photocatalysis to remediate leachates produced in laboratory-scale biocells. Initial characterization revealed critical pollutant levels: COD (1373 mg/L), BOD₅ (378 mg/L), total phosphorus (90 mg/L), ammoniacal nitrogen (201 mg/L), and metals such as Ni, Pb, and Mn levels all exceeding those set out in the Ecuadorian discharge regulations. Optimized EC achieved removal efficiencies of 62.6% for COD, 44.4% for BOD₅, 89.8% for phosphorus, and 86.2% for color. However, residual contamination necessitated a subsequent photocatalytic step. Suspended TiO₂ under UVC irradiation removed up to 81.8% of the remaining COD, 88.7% of the ammoniacal nitrogen, and 94.4% of the phosphorus. Levels of heavy metals such as Zn, Fe, Pb, Mn, and Cu were reduced by over 80%, while Cr⁶⁺ was nearly eliminated. SEM–EDS analysis confirmed successful TiO₂ immobilization on sand substrates, revealing a rough, porous morphology conducive to catalyst adhesion; however, heterogeneous titanium distribution suggests the need for improved coating uniformity. These findings confirm the potential of the EC–TiO₂/UVC hybrid system as an effective and scalable approach for treating complex biocell leachates with reduced chemical consumption. Full article