Global Aerosol Climatology from ICESat-2 Lidar Observations by Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman, Jackson Begolka

“…This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). …”
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STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

“…The dynamic sparse graph convolution gated recurrent unit (DSGCN-GRU) in this model is a novel component that integrates adaptive dynamic sparse graph convolution into the gated recurrent network to simulate the diffusion of information within a dynamic spatial structure. …”
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A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks by Zhiguo Xiao, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu, Dongni Li

“…This architecture achieves the deep fusion of spatio-temporal features through the graph-structural transformation of vibration signals and a feature cascading strategy, thereby improving overall model performance. …”
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Global-Frequency-Domain Network: A Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Fine-Grained Feature Extraction and Global Context Integration by Ye Zhou, Mingyue Zhang, Yechenzi Wang

“…The inherent complex spatial structure and abundant contextual information in these images make segmentation challenges, such as feature recognition difficulties and segmentation discontinuities. …”
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3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross‐window self‐attention by Bin Yu, Quan Zhou, Li Yuan, Huageng Liang, Pavel Shcherbakov, Xuming Zhang

“…Abstract Convolutional neural network (CNN) with the encoder–decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature. …”
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Authenticity Detection of Egg White Powder Using Near-Infrared Spectroscopy Based on Improved One-Dimensional Convolutional Neural Network Model by ZHU Zhihui, LI Wolin, HAN Yutong, JIN Yongtao, YE Wenjie, WANG Qiaohua, MA Meihu

“…Compared with traditional 1D-CNN network structure and other improved algorithms, the EG-1D-CNN model exhibited higher accuracy, faster detection speed, and smaller model footprint, thus making it more suitable for deployment on embedded devices. …”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

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Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach by Justice Williams Asare, Emmanuel Akwah Kyei, Seth Alornyo, Emmanuel Freeman, Martin Mabeifam Ujakpa, William Leslie Brown‐Acquaye, Alfred Coleman, Forgor Lempogo

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ED‐Autoformer: A New Model for Precise Global TEC Forecast by Jiawei Zhou, Hongtao Cai, Xu Yan, Hong‐wen Xu, Kun Hu, Chao Xiong

“…Evaluated on global ionospheric maps TEC, our model achieves a 12.0% improvement (0.51 TECu) in the root mean squared error (RMSE) during solar maximum and an 8.9% improvement (0.14 TECu) in RMSE during solar minimum compared to the Convolutional Long‐Short‐Term Memory (ConvLSTM) method. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…First, investigated the effects of different global information extraction methods on the experimental results; second, analyzed the effects of different modules on the network effects; third, explored the impact of different scales on network performance, sequential cascade structure, and rationalization of hierarchical feature fusion; and fourth, verified the robustness of the enhancement modules designed by testing them on different backbone networks. …”
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GaitRGA: Gait Recognition Based on Relation-Aware Global Attention by Jinhang Liu, Yunfan Ke, Ting Zhou, Yan Qiu, Chunzhi Wang

“…To slove these issues, we propose a gait recognition method based on relational-aware global attention. Specifically, we introduce a Relational-aware Global Attention (RGA) module, which captures global structural information within gait sequences to enable more precise attention learning. …”
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MSA-Net: multiple self-attention mechanism for 3D lung nodule classification in CT images by Jiating Pan, Lishi Liang, Peng Sun, Yongbo Liang, Jianming Zhu, Zhencheng Chen

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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. …”
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A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction by Jiaxin Huang, Huicong Wang, Yuhan Li, Shijian Liu

“…Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. …”
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Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN by Haijun Liu, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu, Yalan Li

“…The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. …”
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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…In remote sensing image building extraction, image regions with similar textures or colors often cause false positives and false negatives in building-detection. Global features can help the model better recognize the overall structure of large buildings and provide contextual background information when segmenting small buildings to avoid mis-segmentation. …”
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HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection by Lei Ding, Chunhui Tang, Yi Fang

“…HGLFNet effectively integrates global semantic context with local detailed information, enhancing the network’s ability to detect thin and occluded lane line structures. …”
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Global Optical and SAR Image Registration Method Based on Local Distortion Division by Bangjie Li, Dongdong Guan, Yuzhen Xie, Xiaolong Zheng, Zhengsheng Chen, Lefei Pan, Weiheng Zhao, Deliang Xiang

“…We further design a Multi-Feature Fusion Capsule Network (MFFCN) that integrates shallow salient features with deep structural details, reconstructing the dimensions of digital capsules to generate feature descriptors encompassing texture, phase, structure, and amplitude information. …”
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MT-SCnet: multi-scale token divided and spatial-channel fusion transformer network for microscopic hyperspectral image segmentation by Xueying Cao, Hongmin Gao, Haoyan Zhang, Shuyu Fei, Peipei Xu, Peipei Xu, Zhijian Wang

“…IntroductionHybrid architectures based on convolutional neural networks and Transformers, effectively captures both the local details and the overall structural context of lesion tissues and cells, achieving highly competitive segmentation results in microscopic hyperspectral image (MHSI) segmentation tasks. …”
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DGCFNet: Dual Global Context Fusion Network for remote sensing image semantic segmentation by Yuan Liao, Tongchi Zhou, Lu Li, Jinming Li, Jiuhao Shen, Askar Hamdulla

“…While Transformer can extract long-range contextual information through multi-head self attention mechanism, which has significant advantages in capturing global feature dependencies. To achieve high-precision semantic segmentation of remote sensing images, this article proposes a novel remote sensing image semantic segmentation network, named the Dual Global Context Fusion Network (DGCFNet), which is based on an encoder-decoder structure and integrates the advantages of CNN in capturing local information and Transformer in establishing remote contextual information. …”
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