Search Results (4,096)

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R²) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R² of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

Purpose: To develop an accelerator neutron source suitable for boron neutron capture therapy—a new promising method for treating malignant tumors—and to develop dosimetry tools and methods. Methods: Research into the transport and acceleration of a beam of charged particles, development and manufacture of an accelerator neutron source, study of the radiation generated, and development and implementation of dosimetry tools and methods. Results: A facility called VITA has been created, which includes a tandem electrostatic accelerator of an original design for producing a 2.3 MeV 10 mA proton beam, a lithium target for generating neutrons in the ⁷Li(p,n)⁷Be reaction, and a beam shaping assembly for forming a therapeutic neutron beam. The facility at the institute is used for scientific research, the facility in Xiamen (China) is used for clinical trials, and the facility in Moscow (Russia) will soon be used for clinical trials. Also, new tools and methods for measuring the boron dose, γ-ray dose, and sum of the fast neutron dose and the nitrogen dose have been proposed and implemented. The conducted studies demonstrated the high efficiency of the VITA^® facility, the first possibility of implementing prompt γ-ray spectroscopy for boron imaging, and the first possibility of implementing lithium neutron capture therapy, which has advantages over BNCT, and also presented the results of the development of new tools and methods for measuring the boron dose, γ-ray dose, and the sum of the fast neutron dose and the nitrogen dose. Conclusions: The authors strongly recommend using prompt γ-ray spectroscopy in treatment and developing lithium neutron capture therapy, including in combination with BNCT, and note the high efficiency, reliability and compactness of the VITA^® facility. Full article

Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multimodal information extraction and knowledge graph construction is proposed, enabling structured processing of heterogeneous data from multiple sources. For the image modality, positional and semantic information is extracted using YOLOv8n and Optical Character Recognition (OCR) techniques. To mitigate the effects of uncertain connection topology and noise interference, a Heuristic Circular Stepping Search Algorithm (HCSA) is designed to achieve deterministic path tracing of information flows. For the text modality, a RoFormer-BiLSTM-CRF model enhanced with Rotary Position Embedding (RoPE) is developed to alleviate information degradation in long-sequence texts, thereby enabling high-accuracy extraction of entities and relationships. Furthermore, by combining the domain ontology mapping rules and string similarity, the extracted device entities from the two modalities are aligned, thereby converting scattered data into a structured knowledge graph. Experiments conducted on the secondary-side data of a substation in China demonstrate that the proposed method effectively extracts multimodal information from substation secondary systems, providing valuable support for information management and decision-making assistance in complex industrial systems. Full article

As AI permeates daily life, algorithmic platforms increasingly function as complex sociotechnical systems that shape public perception and societal attitudes. Addressing concerns that AI text-to-image models and search engines reinforce stereotypes, this study focuses on China, a context marked by traditional gender norms and a vast technological ecosystem, examining how algorithmic systems perpetuate gender power structures through occupational representations. Using algorithmic audits of 60 occupations, Z-tests, and QAP network analysis, this study compares platform gender representations with national census data, systematically distinguishing “generative bias” in AI platforms (Doubao Seedream 3.0, Jimeng Image 3.0) from “retrieval bias” in search engines (Baidu, Sogou). Findings reveal that search engines reinforce stereotypes by over-representing dominant genders and obscuring non-mainstream ones. Generative AI exhibits more radical distortions. The specialized AI Jimeng shows a strong gender polarization feature, while the general AI Doubao shows an ideal balanced gender presentation tendency, balancing representation yet creating an equally false reality. Compared to search engines, AI platforms have greater creativity in representing occupational gender. This study reveals a mutually reinforcing bias cycle among audiences, media, and algorithms, offering a crucial non-Western perspective for feminist technology studies and significant implications for equitable AI governance. Full article

The intertidal beach profile provides a fundamental representation of beach morphology and serves as a key indicator of shoreline morphodynamics. To enable frequent and accurate mapping of intertidal beach profiles, this study proposes an automated reconstruction framework that integrates single-pixel image columns with a stacked bidirectional long short-term memory (Bi-LSTM) network. Time-exposure imagery, commonly referred to as Timex imagery, acquired from a shore-based video monitoring station at Xisha Bay, China, is used as the primary data source, while wave records obtained from a wave buoy are incorporated to assign elevations to the detected waterline breakpoints, thereby enabling automatic beach profile reconstruction. The stacked Bi-LSTM network is trained for land–sea segmentation and waterline breakpoint localization. achieving the best performance among the tested methods, with precision, recall, accuracy, and F1 score values of 0.951, 0.894, 0.978, and 0.903, respectively, and a mean breakpoint localization error of 2.23 pixels. Breakpoint elevations were then estimated using a local slope–wave setup attribution model. Validation against field-measured topographic data from four fixed profiles and three survey periods showed good agreement between the reconstructed and measured profiles, with a period-based root mean square error (RMSE) of 0.212 ± 0.080 m. When all validation points were combined, the reconstructed elevations showed strong agreement with the measured elevations, with a coefficient of determination (R²) of 0.988 and an overall RMSE of 0.24 m. The profile comparisons further showed that the reconstructed profiles generally captured the overall profile shape and cross-shore morphological pattern of the measured profiles, although reconstruction accuracy varied among the four fixed profiles. These differences demonstrate that camera viewing angle, field-of-view position, camera-to-profile distance, and image quality are important factors influencing video-derived beach profile reconstruction. These results indicate that the proposed method can directly reconstruct fixed intertidal beach profiles from shore-based Timex imagery without generating a digital elevation model of the entire intertidal zone. It provides a practical tool for high-frequency monitoring of intertidal profile morphology and supports the quantitative analysis of beach erosion–accretion dynamics. Full article

Small, dispersed, and vegetated creeping landslides in hilly areas of eastern China hinder traditional remote sensing and detection. To address this, this study takes Yixian County (Anhui Province) as a representative area. Based on high-resolution GF-2 satellite images, three deep learning models embedded with the Squeeze-and-Excitation (SE) attention mechanism (ResNet18-SE, VGG13-SE, UNet-SE) were developed and compared with the original UNet model. Combined with field investigation, landslide mapping and accuracy assessment were conducted to evaluate the feature extraction capabilities of four models. The results indicate that the UNet-SE model achieved optimal performance (Precision: 0.911, Recall: 0.685, F1-score: 0.782, Kappa: 0.730, IoU: 0.643). Its F1-score exceeds ResNet18-SE, VGG13-SE, and the original UNet by 8%, 3%, and 5%, respectively, proving superior regional adaptability and generalization performance. Additional verification on creeping landslides in Kecun Town (Yixian County) and post-earthquake landslides in Lushan County (Sichuan Province) further confirms the reliability of the UNet-SE model. Furthermore, Frequency Ratio (FR), Random Forest (RF), and SHapley Additive exPlanations (SHAP) were adopted to reveal the correlation between landslide occurrence and seven geological-environmental factors. Landslides are most susceptible to develop at elevations of 400–500 m, NDVI values of 0.4–0.5, slopes below 10°, east and northeast aspects, 300–500 m away from rivers, 500–1000 m away from faults, and areas covered by soft sedimentary lithology. Distance from faults, distance from rivers, and elevation are identified as the three favorable conditional factors. In conclusion, the proposed landslide detection framework can provide reliable spatial data and technical references for regional geological hazard prevention, ecological conservation and sustainable development in hilly areas. Full article

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional manual disaster investigation is inefficient because the pipelines are widely distributed, access is limited and the terrain may be rugged. Therefore, efficient and accurate disaster identification and risk assessment have become a priority that the industry urgently needs to address. Taking the Jiangxi section of the West Line II Zhangshu–Xiangtan connection line as the research area, this study combines the SBAS-InSAR technology with spatial analysis based on GIS to support early disaster identification, surface deformation monitoring and vulnerability assessment. The analysis of 48 Sentinel-1A satellite images shows that the regional ground deformation range is −19.5 to 19.1 mm per year, and most areas show a slow deformation of within ±10 mm per year. The preliminary visual interpretation of the SBAS-InSAR ground deformation data yields 121 preliminary high-deformation disaster points. Combined with the 9 key assessment factors in the GIS platform and the entropy-weighted information model obtained from the geological disaster susceptibility evaluation map and using the optical remote sensing images, 21 human interference points are excluded, and finally 100 potential geological disaster hazard areas are retained. Field verification was conducted through ground reconnaissance surveys and confirmed that 78 of these areas have geological disaster hazards such as landslide, collapses, and slope water damage, providing solid technical support for geological disaster management, monitoring and early warning along the pipeline route. This study proposes a multi-source integrated framework combining SBAS-InSAR, GIS-based susceptibility assessment, and optical validation for improving the reliability of early geological hazard identification. Full article

Traditional handmade papers from China, Japan, and Korea, including Xuan paper, Washi, and Hanji, are difficult to distinguish visually because they share cellulose-rich compositions and similar appearances. This study applied near-infrared hyperspectral imaging (NIR-HSI) and machine-learning classifiers to identify selected traditional handmade papers by country and product type. Spectra in the 1250–1700 nm region were analyzed using k-nearest neighbors, support vector machines, and artificial neural networks. The models achieved high classification performance, with F1-scores of up to 1.000, and Y-scrambling confirmed that the results were not attributable to random class assignment. SHAP analysis identified important wavelength regions near 1256, 1360, 1404, 1449, 1537, 1576, 1635, and 1685 nm, which were associated with C–H, O–H, phenolic, hydrogen-bonded polysaccharide, and lignin-related vibrations. These bands varied among paper groups and provided chemically meaningful information for classification, while SAM visualization revealed pixel-level spectral similarity. These results show that NIR-HSI provides a compact, nondestructive, and interpretable approach for classifying selected East Asian handmade papers. Full article

Accurate identification of tea diseases and pests is a key challenge in smart agriculture. Current approaches to tea disease and pest identification suffer from a scarcity of high-quality annotated image data and poor generalization of existing models in real-world field environments. To address these issues, this paper first constructs and releases a dataset of images of tea diseases and pests captured in real-world field scenarios. The dataset uses leaf-level annotations and covers six common tea disease and pest categories in Guizhou Province, China. It contains 549 high-resolution images covering varying lighting conditions, backgrounds, and disease severity levels. Based on this dataset, we propose a convolutional neural network model named GatedFCA-YOLO, which integrates a small-object detection layer with an adaptive attention mechanism. Specifically, the small-object detection layer preserves high-resolution details, effectively improving recall of minute lesions. Meanwhile, the GatedFCA module is designed to fuse a spatial gating mechanism with FCAttention. It enables adaptive feature enhancement and significantly boosts the model’s recognition robustness under complex backgrounds. Experimental results on our dataset show that GatedFCA-YOLO achieves 78.9% mAP@0.5, which is 3% increased compared to the baseline model YOLO11n, thereby verifying the effectiveness of the proposed method. Full article

Berry thinning is a fundamental operation in modern vineyard management, and future robotic thinning systems have the potential to reduce labor intensity and improve operational consistency. However, automated berry thinning under field conditions is still constrained by insufficient berry-level segmentation accuracy, difficulty in recognizing occluded berries, and high missed-detection rates for small berries. These limitations mainly arise from dense berry arrangements, severe mutual occlusion, and the subtle visual features of small targets. To address these challenges, this study developed a lightweight grape berry instance segmentation and thinning decision-support method based on YOLOv8n-seg. A two-stage knowledge distillation strategy, using Mask R-CNN and YOLOv8l-seg as teacher models, was combined with 30% backbone pruning to improve the recognition of occluded and small berries while maintaining model efficiency. Subsequently, the DBSCAN clustering algorithm was used to analyze berry centroid coordinates and equivalent diameters extracted from instance segmentation masks, thereby generating preliminary thinning-target recommendations based on local berry density and berry size. The model was trained and evaluated on a self-constructed dataset containing 330 valid grape bunch images collected in 2025 from Yongming Vineyard, Lin’an District, Hangzhou, Zhejiang Province, China. The results showed that the optimized YOLOv8n-seg model achieved a box $mAP50\text{-}95$ of 0.8945 and a mask $mAP50\text{-}95$ of 0.7910, with an inference speed of 119.19 $FPS$ and 3.26 M parameters on an NVIDIA RTX 3060 Laptop GPU. Compared with the original YOLOv8n-seg model, the optimized model improved mask $mAP50\text{-}95$ by 1.20 percentage points, increased inference speed by 71.79 $FPS$, and reduced the number of parameters by 2.38 M. These results indicate that the proposed method improves grape berry instance segmentation performance while achieving a favorable balance among segmentation accuracy, lightweight characteristics, and inference efficiency. The proposed framework provides an offline RGB-based visual perception and preliminary thinning decision-support method for future grape berry thinning robots. However, because the current dataset was collected from Shine Muscat grape bunches at the berry enlargement stage in a single vineyard using the same imaging setup, the results should be interpreted as preliminary evidence under the specific cultivar, growth stage, vineyard, and imaging conditions of this study. Further validation across different grape cultivars, growth stages, vineyards, production seasons, camera systems, embedded platforms, and real robotic thinning operations is still required. Full article

The Lutan-1 (LT-1) mission is the first civilian L-band differential interferometric synthetic aperture radar (SAR) system in China, with interferometry as its primary application. The system comprises two multi-polarimetric satellites, LT-1A and LT-1B. For the purpose of quantitative application from SAR images of Lutan-1 satellites, the relationship between the SAR image intensity and the backscattering coefficient of ground objects should be established by radiometric calibration. Field radiometric calibration provides absolute calibration constants, but it suffers from beam coverage. Internal on-board calibration, by contrast, tracks relative changes in radiometric performance but cannot yield absolute calibration constants. Therefore, we develop a method that combines on-board internal calibration with field radiometric calibration to monitor the radiometric performance of LT-1 satellites and to analyze the variation patterns revealed by both internal and field calibrations. We monitor the amplitude and phase trend of internal calibration, calculate absolute calibration constants from field calibration, and refine and evaluate the absolute calibration constants. We analyzed the internal calibration data and SAR calibration data of the LT-1 satellite from 2023 to 2025. The results show that the TRMs of the LT-1 satellite exhibit a slight decline over time, and the magnitude of the decrease in LT-1B is greater than that of LT-1A. The slight decrease in internal calibration has not yet led to visible changes in the absolute calibration constant for LT-1A, while the absolute calibration constants decrease slightly for LT-1B. After removing the calibration constant outliers and correcting the gain difference among the beams for the LT-1A satellite, absolute radiometric accuracy is improved from 0.40 dB (1σ) to 0.25 dB (1σ). The absolute radiometric accuracy of the LT-1B satellite is 0.38 dB (1σ). It gives a reference for radiometric performance monitoring of the SAR satellite over a long period. Full article

Wetland landscape classification is fundamental to monitoring changes in ecosystem patterns. This study proposes an ensemble classification approach that integrates Maximum Likelihood Classification (MLC) and Decision Tree (DT) methods with optimized feature selection for long-term wetland monitoring in the Liaohe Delta, China. Based on four periods of Landsat remote sensing images from 1987, 1997, 2007, and 2017, multi-dimensional features including PCA1, TCT, NDVI, and MNDWI were extracted to construct a hierarchical classification system comprising 13 landscape types. The results show that the integrated method achieved an overall classification accuracy of 87.71% and a Kappa coefficient of 0.85, improving by 16.50% and 19.72%, respectively, compared with the single MLC approach. The classification results reveal significant spatiotemporal variations in landscape patterns. Reed wetlands decreased from 1284.44 km² in 1987 to 1006.70 km² in 1997, followed by a recovery to 1275.53 km² in 2017. In contrast, Suaeda communities experienced severe degradation, declining sharply from 227.48 km² in 1987 to 30.52 km² in 2017. Meanwhile, the coastline advanced landward by 263.24 km², with the proportion of artificial shoreline increasing from 12.4% to 38.7%. The changes in wetland landscape types in the Liaohe Delta from 1987 to 2017 were mainly influenced by urbanization processes and ecological restoration policies. These findings indicate that the proposed method can effectively support long-term wetland landscape dynamics analysis and provide a useful reference for coastal wetland management. Full article

Public emergencies are increasingly shared and discussed on short-video platforms, where videos, user interactions, and timing influence how social risks become visible and spread. This study examines a major short-video platform in China and proposes a Multimodal Information Risk Flow (MIRF) framework to understand how risk signals appear, are categorized, and evolve over time during emergencies. We analysed 250,100 anonymized video–comment records collected between 2022 and 2024, combining text, images, audio, user behavior, and author information to study patterns of risk representation and amplification. Our results show that social risk spreads unevenly over time and is strongly influenced by the type of content, with toxic comments leading to faster and larger cascades. General engagement metrics play a smaller role, and shorter times to peak activity are consistently linked to larger risk cascades. These findings highlight that social risk on short-video platforms is multimodal and temporally concentrated, and caution is needed when generalizing results beyond similar platforms and regulatory contexts. Full article