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学者姓名：邬群勇
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Building type information indicates the functional properties of buildings and plays a crucial role in smart city development and urban socioeconomic activities. Existing methods for classifying building types often face challenges in accurately distinguishing buildings between types while maintaining well-delineated boundaries, especially in complex urban environments. This study introduces a novel framework, i.e., CNN-Transformer cross-attention feature fusion network (CTCFNet), for building type classification from very high resolution remote sensing images. CTCFNet integrates convolutional neural networks (CNNs) and Transformers using an interactive cross-encoder fusion module that enhances semantic feature learning and improves classification accuracy in complex scenarios. We develop an adaptive collaboration optimization module that applies human visual attention mechanisms to enhance the feature representation of building types and boundaries simultaneously. To address the scarcity of datasets in building type classification, we create two new datasets, i.e., the urban building type (UBT) dataset and the town building type (TBT) dataset, for model evaluation. Extensive experiments on these datasets demonstrate that CTCFNet outperforms popular CNNs, Transformers, and dual-encoder methods in identifying building types across various regions, achieving the highest mean intersection over union of 78.20% and 77.11%, F1 scores of 86.83% and 88.22%, and overall accuracy of 95.07% and 95.73% on the UBT and TBT datasets, respectively. We conclude that CTCFNet effectively addresses the challenges of high interclass similarity and intraclass inconsistency in complex scenes, yielding results with well-delineated building boundaries and accurate building types.
Keyword ：
Accuracy Accuracy Architecture Architecture Buildings Buildings Building type classification Building type classification CNN-transformer networks CNN-transformer networks cross-encoder cross-encoder Earth Earth Feature extraction Feature extraction feature interaction feature interaction Optimization Optimization Remote sensing Remote sensing Semantics Semantics Transformers Transformers very high resolution remote sensing very high resolution remote sensing Visualization Visualization
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| GB/T 7714 | Zhang, Shaofeng , Li, Mengmeng , Zhao, Wufan et al. Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 976-994 . |
| MLA | Zhang, Shaofeng et al. "Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 976-994 . |
| APA | Zhang, Shaofeng , Li, Mengmeng , Zhao, Wufan , Wang, Xiaoqin , Wu, Qunyong . Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 976-994 . |
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In the construction of smart cities in the new era, traffic prediction is an important component. Precise traffic flow prediction faces significant challenges due to spatial heterogeneity, dynamic correlations, and uncertainty. Most existing methods typically learn from a single spatial or temporal perspective, or at best combine the two in a limited dual-perspective manner, which limits their ability to capture complex spatio-temporal relationships. In this paper, we propose a novel Multi-view Spatio-Temporal Dynamic Fusion Graph Convolutional Recurrent Network (MSTDFGRN) to address these limitations. The core idea is to learn dynamic spatial dependencies alongside both short- and long-term temporal patterns through multi-view learning. First, we introduce a multi-view spatial convolution module that dynamically fuses static and adaptive graphs in multiple subspaces to learn intrinsic and potential spatial dependencies of nodes. Simultaneously, in the temporal view, we design both short-range and long-range recurrent networks to aggregate spatial domain knowledge of nodes at multiple granularities and capture forward and backward temporal dependencies. Furthermore, we design a spatiotemporal attention model that applies an attention mechanism to each node, capturing global spatio-temporal dependencies. Comprehensive experiments on four real traffic flow datasets demonstrate MSTDFGRN's excellent predictive accuracy. Specifically, compared to the Spatial- Temporal Graph Attention Gated Recurrent Transformer Network model, our method improves the MAE by 4.69% on the PeMS08 dataset.
Keyword ：
Graph Convolutional Network Graph Convolutional Network Multi-view learning Multi-view learning Spatio-temporal dependencies Spatio-temporal dependencies Traffic flow prediction Traffic flow prediction
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| GB/T 7714 | Yang, Shiyu , Wu, Qunyong , Wang, Yuhang et al. MSTDFGRN: A Multi-view Spatio-Temporal Dynamic Fusion Graph Recurrent Network for traffic flow prediction [J]. | COMPUTERS & ELECTRICAL ENGINEERING , 2025 , 123 . |
| MLA | Yang, Shiyu et al. "MSTDFGRN: A Multi-view Spatio-Temporal Dynamic Fusion Graph Recurrent Network for traffic flow prediction" . | COMPUTERS & ELECTRICAL ENGINEERING 123 (2025) . |
| APA | Yang, Shiyu , Wu, Qunyong , Wang, Yuhang , Zhou, Zhan . MSTDFGRN: A Multi-view Spatio-Temporal Dynamic Fusion Graph Recurrent Network for traffic flow prediction . | COMPUTERS & ELECTRICAL ENGINEERING , 2025 , 123 . |
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Accurate traffic forecasting is essential for smart city development. However, existing spatiotemporal modeling methods often face significant challenges, including limitations in handling complex temporal dependencies, capturing multiscale spatial relationships, and modeling the interaction between temporal and spatial features. These challenges arise due to the reliance on extended historical data, fixed adjacency matrices, and the lack of dynamic spatiotemporal interaction modeling. To address these issues, we propose the Spatiotemporal Dual-Scale Interaction Network (SDSINet). SDSINet introduces an implicit temporal information enhancement method that embeds temporal identity information into feature representations, reducing the computational overhead and improving the modeling of global temporal features. Additionally, SDSINet integrates a dynamic multiscale spatial modeling approach that combines adaptive and scale-specific graphs, enabling the model to capture both local and global spatial dependencies. Furthermore, SDSINet incorporates a dual-scale spatiotemporal interaction learning framework that captures short-term and long-term temporal dependencies as well as multiscale spatial correlations. Extensive experiments on real-world datasets - traffic flow (PeMS04), speed (PeMSD7(M)), and demand (NYCBike Drop-off/Pick-up) - demonstrate that SDSINet outperforms existing state-of-the-art methods in prediction accuracy and computational efficiency. Notably, SDSINet achieves a 14.03% reduction in MAE on the NYCBike Drop-off dataset compared to AFDGCN, setting anew benchmark for traffic forecasting.
Keyword ：
Graph convolutional network Graph convolutional network Interactive learning Interactive learning Spatiotemporal dependencies Spatiotemporal dependencies Traffic prediction Traffic prediction
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| GB/T 7714 | Yang, Shiyu , Wu, Qunyong . SDSINet: A spatiotemporal dual-scale interaction network for traffic prediction [J]. | APPLIED SOFT COMPUTING , 2025 , 173 . |
| MLA | Yang, Shiyu et al. "SDSINet: A spatiotemporal dual-scale interaction network for traffic prediction" . | APPLIED SOFT COMPUTING 173 (2025) . |
| APA | Yang, Shiyu , Wu, Qunyong . SDSINet: A spatiotemporal dual-scale interaction network for traffic prediction . | APPLIED SOFT COMPUTING , 2025 , 173 . |
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Accurate information on cloud amount vertical structure is crucial for weather monitoring and understanding climate systems. Active sensors from satellites can provide three-dimensional (3D) cloud structure but with limited geographical coverage, passive sensors from satellites have expanded observation coverage but with limited capability on profiling the clouds. Combing active and passive observations from satellites, together with atmospheric reanalysis data, this study proposes a machine learning approach (CLANN, CLoud Amount Neural Network) to construct three-dimensional (3D) cloud amounts at passive observational coverage. Independent validation is conducted for cloud amount estimates derived from combined data of the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun-4 A and ERA5 using CALIPSO/CALIOP product as reference. The results indicate notable correlations (Pearson's r = 0.73). The cloud-amount-weighted height showed a high consistency in terms of height positioning between CLANN estimations and CALIOP data, with an RMSE of 1.88 km and a Pearson's r of 0.92. Key features such as water vapor band brightness temperature and upper-layer temperature significantly enhanced model accuracy, as revealed by permutation importance analysis. Sensitivity tests highlighted the critical role of the 1.375 mu m band in cirrus altitude detection, justifying the model's reliance on daytime observations. Additionally, the 3D statistical results from CLANN in 2019 reveal the seasonal variation details of cloud distribution, further demonstrating its application value in climate analysis.
Keyword ：
3D cloud structure 3D cloud structure Advanced geostationary imager Advanced geostationary imager Cloud amount estimation Cloud amount estimation Cloud seasonal variation Cloud seasonal variation Neural network Neural network
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| GB/T 7714 | Lin, Han , Li, Jun , Min, Min et al. CLANN: Cloud amount neural network for estimating 3D cloud from geostationary satellite imager [J]. | REMOTE SENSING OF ENVIRONMENT , 2025 , 318 . |
| MLA | Lin, Han et al. "CLANN: Cloud amount neural network for estimating 3D cloud from geostationary satellite imager" . | REMOTE SENSING OF ENVIRONMENT 318 (2025) . |
| APA | Lin, Han , Li, Jun , Min, Min , Zhang, Feng , Wang, Keyue , Wu, Qunyong . CLANN: Cloud amount neural network for estimating 3D cloud from geostationary satellite imager . | REMOTE SENSING OF ENVIRONMENT , 2025 , 318 . |
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The flow of essential elements such as people, goods, and information through complex networks has become a critical factor in shaping urban dynamics and regional development. Quantifying location centrality plays an indispensable role not only in urban infrastructure planning but also in National central city planning. Two vital aspects should be considered for central nodes in flow-based complex networks: their impact on adjacent nodes and the diversity of nodes they affect. In this paper, we present a centrality measure index (C-index) that accounts for flow volume and flow directions, offering a high degree of interpretability. We applied the C-index to four public weighted complex networks, demonstrating that our method outperforms classical methods. Furthermore, we validated the effectiveness and advantages of C-index on quantifying location centrality both in inter-city and intra-city population mobility network. The centrality findings from the perspective of population mobility can reinforce guidelines for understanding National central cities and polycentric structure of cities, thereby facilitating policy-making of sustainable urban development.
Keyword ：
Centrality measure Centrality measure Interactions between locations Interactions between locations Population mobility networks Population mobility networks Sustainable urban development Sustainable urban development
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| GB/T 7714 | Yin, Yanzhong , Wu, Qunyong , Zhao, Zhiyuan et al. Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development [J]. | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS , 2025 , 116 . |
| MLA | Yin, Yanzhong et al. "Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development" . | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 116 (2025) . |
| APA | Yin, Yanzhong , Wu, Qunyong , Zhao, Zhiyuan , Chen, Xuanyu . Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development . | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS , 2025 , 116 . |
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The circulation of tropical cyclones (TCs) exerts a multifaceted influence on the spatial and temporal distribution of surface pollutants. This study investigates the response of surface ozone (O3) concentration to the TCs in Fujian Province from June to December 2022 by analyzing the contributions of atmospheric pollutants, meteorological conditions, and dynamical transports. Empirical orthogonal function (EOF) decomposition methods are used to analyze the spatio-temporal distribution patterns of affected O3, and a Gradient Boosting Regression Trees (GBRT) machine learning model is employed to estimate surface O3 concentration, quantifying the influence of each factor. The results indicate an anomaly increase in O3 concentration during this period, with photochemistry-related meteorological conditions being the primary influencer, accounting for 66.9% of O3 variations, elucidating the interpretability of the GBRT model for attributing changes in O3 concentration. Low relative humidity and high temperature conditions have been identified as pivotal factors influencing the rise in O3 concentrations. The presence of TC undermines this predominant influence, amplifying the role of transport factors and other atmospheric pollutants. In the case studies of TC (Muifa and Nanmadol, 2022), the slow or stagnant TCs triggered persistent downdrafts in its periphery and brought favorable meteorological conditions such as clear sky and warm temperature for photochemistry. TCs also enhances the impact of horizontal and vertical dynamic transport on O3 concentrations. This work provides vital insights into the complex interplay between TCs and surface O3 concentrations, highlighting the need for targeted environmental and air quality management strategies in regions frequently impacted by TCs.
Keyword ：
Attribution analysis Attribution analysis Gradient boosting regression trees Gradient boosting regression trees Surface ozone concentration Surface ozone concentration Tropical cyclone Tropical cyclone
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| GB/T 7714 | Wang, Keyue , Zhao, Rui , Wu, Qunyong et al. Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China [J]. | ATMOSPHERIC POLLUTION RESEARCH , 2025 , 16 (1) . |
| MLA | Wang, Keyue et al. "Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China" . | ATMOSPHERIC POLLUTION RESEARCH 16 . 1 (2025) . |
| APA | Wang, Keyue , Zhao, Rui , Wu, Qunyong , Li, Jun , Wang, Hong , Lin, Han . Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China . | ATMOSPHERIC POLLUTION RESEARCH , 2025 , 16 (1) . |
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以深圳市为研究区，使用工作日早高峰的接驳骑行订单数据，考虑地铁站和出入口两层面的潮汐均衡性分类，从单车调度角度将出入口划分为无需调度型、站内调度型和站外调度型；使用非负矩阵分解方法划分出入口流量时空分布类别，确定调度时间.结果表明，将接驳轨道交通的潮汐均衡分析聚焦到出入口层面，精确划分出入口调度类型，可确定调度方式和产生调度需求的时间，为单车管理者进行车辆调度提供可靠的数据支持.
Keyword ：
共享单车 共享单车 地铁出入口 地铁出入口 接驳 接驳 潮汐均衡性 潮汐均衡性 非负矩阵分解 非负矩阵分解
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| GB/T 7714 | 王涵菁 , 邬群勇 , 邝嘉恒 et al. 接驳轨道交通的共享单车潮汐均衡时空分布模式 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (02) : 176-183 . |
| MLA | 王涵菁 et al. "接驳轨道交通的共享单车潮汐均衡时空分布模式" . | 福州大学学报(自然科学版) 52 . 02 (2024) : 176-183 . |
| APA | 王涵菁 , 邬群勇 , 邝嘉恒 , 郑汉捷 , 张晨 , 尹延中 . 接驳轨道交通的共享单车潮汐均衡时空分布模式 . | 福州大学学报(自然科学版) , 2024 , 52 (02) , 176-183 . |
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With the COVID-19 epidemic having a profound impact on global population mobility patterns, this study aims to explore the new pattern of population mobility in China after the end of the epidemic. Based on Baidu migration data, this study uses the Intercity Population Transcendence Index (S-index) and Louvain Community Discovery Algorithm to calculate urban centrality and detect communities in 366 cities in China. The study found that: after the full unsealing of the epidemic, urban centrality rose overall; rankings in the west and northeast generally rose, while rankings in the central and southeastern coastal regions fell; rankings in the south and north generally rose, and cities in the central region fell; the number of cohesive communities fell, and communities became more connected, with interprovincial fringe cities interacting more closely with neighbouring provinces. © 2024 Copyright held by the owner/author(s).
Keyword ：
Community Detection Community Detection Population Flows Population Flows S-index S-index Urban Centrality Urban Centrality
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| GB/T 7714 | Chen, X. , Yin, Y. , Wu, Q. . Revealing Population Flows in Community and Urban Centrality Perspective: From Lockdown to Recovery in China [未知]. |
| MLA | Chen, X. et al. "Revealing Population Flows in Community and Urban Centrality Perspective: From Lockdown to Recovery in China" [未知]. |
| APA | Chen, X. , Yin, Y. , Wu, Q. . Revealing Population Flows in Community and Urban Centrality Perspective: From Lockdown to Recovery in China [未知]. |
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Traffic prediction is indispensable for constructing transportation networks in smart cities. Due to the complex spatio-temporal correlations of traffic data, this task presents challenges. Recent studies mainly use graph neural networks to simulate complex spatio-temporal relationships through fixed or adaptive graphs. While fixed graphs may not adapt to data drift caused by changes in road network structures, adaptive graphs overlook critical information of the original roads. To address this challenge, we propose a principal spatio-temporal causal graph convolutional network (PSTCGCN) to accurately predict traffic flow. In response to the data drift problem, we introduce a data-driven semi-principal generated graph embedding (SPGGE) that first extracts the principal features of the original roads to model the spatio-temporal sequence distribution and then remodels the data after drift through adaptive transformation. Traffic flow data, while showcasing fundamental spatial relationships, also exhibit temporal dynamics. We propose an effective temporal causal convolution component comprising SPGGE, graph convolution, both local and global causal learning models to jointly learn short-term and long-term spatio-temporal correlations. PSTCGCN was evaluated using two actual highway datasets, PEMS03 and PEMS07, achieving a notable improvement of 6.12% in RMSE on PEMS03 compared to STGATRGN. Our code is available at https://github.com/OvOYu/PSTCGCN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keyword ：
Data drift Data drift Graph convolution network Graph convolution network Principal component analysis Principal component analysis Traffic flow prediction Traffic flow prediction
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| GB/T 7714 | Yang, S. , Wu, Q. , Li, Z. et al. PSTCGCN: Principal spatio-temporal causal graph convolutional network for traffic flow prediction [J]. | Neural Computing and Applications , 2024 . |
| MLA | Yang, S. et al. "PSTCGCN: Principal spatio-temporal causal graph convolutional network for traffic flow prediction" . | Neural Computing and Applications (2024) . |
| APA | Yang, S. , Wu, Q. , Li, Z. , Wang, K. . PSTCGCN: Principal spatio-temporal causal graph convolutional network for traffic flow prediction . | Neural Computing and Applications , 2024 . |
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Current research often formalizes traffic prediction tasks as spatio-temporal graph modeling problems. Despite some progress, this approach still has the following limitations. First, space can be divided into intrinsic and latent spaces. Static graphs in intrinsic space lack flexibility when facing changing prediction tasks, while dynamic relationships in latent space are influenced by multiple factors. A deep understanding of specific traffic patterns in different spaces is crucial for accurately modeling spatial dependencies. Second, most studies focus on correlations in sequential time periods, neglecting both reverse and global temporal correlations. This oversight leads to incomplete temporal representations in models. In this work, we propose a Space-Specific Graph Convolutional Recurrent Transformer Network (SSGCRTN) to address these limitations simultaneously. For the spatial aspect, we propose a space-specific graph convolution operation to identify patterns unique to each space. For the temporal aspect, we introduce a spatio-temporal interaction module that integrates spatial and temporal domain knowledge of nodes at multiple granularities. This module learns and utilizes parallel spatio-temporal relationships between different time points from both forward and backward perspectives, revealing latent patterns in spatio-temporal associations. Additionally, we use a transformer-based global temporal fusion module to capture global spatio-temporal correlations. We conduct experiments on four real-world traffic flow datasets (PeMS03/04/07/08) and two traffic speed datasets (PeMSD7(M)/(L)), achieving better performance than existing technologies. Notably, on the PeMS08 dataset, our model improves the MAE by 6.41% compared to DGCRN. The code of SSGCRTN is available at https://github.com/OvOYu/SSGCRTN.
Keyword ：
Graph convolutional network Graph convolutional network Spatio-temporal dependencies Spatio-temporal dependencies Traffic prediction Traffic prediction Transformer Transformer
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| GB/T 7714 | Yang, Shiyu , Wu, Qunyong , Wang, Yuhang et al. SSGCRTN: a space-specific graph convolutional recurrent transformer network for traffic prediction [J]. | APPLIED INTELLIGENCE , 2024 , 54 (22) : 11978-11994 . |
| MLA | Yang, Shiyu et al. "SSGCRTN: a space-specific graph convolutional recurrent transformer network for traffic prediction" . | APPLIED INTELLIGENCE 54 . 22 (2024) : 11978-11994 . |
| APA | Yang, Shiyu , Wu, Qunyong , Wang, Yuhang , Lin, Tingyu . SSGCRTN: a space-specific graph convolutional recurrent transformer network for traffic prediction . | APPLIED INTELLIGENCE , 2024 , 54 (22) , 11978-11994 . |
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