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Classification of urban interchange patterns using a model combining shape context descriptor and graph convolutional neural network

文献类型：期刊论文

作者Yang, Min; Cao, Minjun; Cheng, Lingya; Jiang, Huiping1,4; Ai, Tinghua; Yan, Xiongfeng2
刊名GEO-SPATIAL INFORMATION SCIENCE
出版日期2023-10-23
卷号N/A
关键词Road networks interchange pattern classification Graph Convolutional Neural Networks (GCNNs) Shape Context (SC) descriptor
DOI10.1080/10095020.2023.2264337
文献子类Article ; Early Access
英文摘要Pattern recognition is critical to map data handling and their applications. This study presents a model that combines the Shape Context (SC) descriptor and Graph Convolutional Neural Network (GCNN) to classify the patterns of interchanges, which are indispensable parts of urban road networks. In the SC-GCNN model, an interchange is modeled as a graph, wherein nodes and edges represent the interchange segments and their connections, respectively. Then, a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes. Finally, a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns. The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap. The classification accuracy was 87.06%, which was higher than that of the image-based AlexNet, GoogLeNet, and Random Forest models.
WOS研究方向Remote Sensing
WOS记录号WOS:001091599000001
源URL[http://ir.igsnrr.ac.cn/handle/311030/200910]  
专题区域可持续发展分析与模拟院重点实验室_外文论文
作者单位1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China
2.Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China
3.Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China
4.Inst Geog Sci & Nat Resource Res, Chinese Acad Sci, Key Lab Reg Sustainable Dev Modeling, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Min,Cao, Minjun,Cheng, Lingya,et al. Classification of urban interchange patterns using a model combining shape context descriptor and graph convolutional neural network[J]. GEO-SPATIAL INFORMATION SCIENCE,2023,N/A.
APA Yang, Min,Cao, Minjun,Cheng, Lingya,Jiang, Huiping,Ai, Tinghua,&Yan, Xiongfeng.(2023).Classification of urban interchange patterns using a model combining shape context descriptor and graph convolutional neural network.GEO-SPATIAL INFORMATION SCIENCE,N/A.
MLA Yang, Min,et al."Classification of urban interchange patterns using a model combining shape context descriptor and graph convolutional neural network".GEO-SPATIAL INFORMATION SCIENCE N/A(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

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