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Geographic Information Linking Method Based on Machine Learning

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DOI: 10.23977/infkm.2022.030204 | Downloads: 8 | Views: 456


Jun Shu 1


1 Wuhan University of Engineering Science, Wuhan, Hubei, 430200, China

Corresponding Author

Jun Shu


With the continuous development of Internet geographic information(GI) system, various GI sources have sprung up. However, the representation and storage methods of GI of these GI data sources are different, resulting in significant differences in the accuracy and integrity of GI, which has caused many difficulties for GI integration. Therefore, this paper introduces the importance of geospatial relationship in GI data link, and discusses the GI link method based on machine learning(ML); The link results of support vector machine classification method and k-nearest neighbor classification method in GI data link method are discussed respectively. Both of them have achieved good experimental results, and the differences between them are analyzed in detail. At the same time, the GI data link method in this paper is compared with the geographic context related information link method using graph theory method. The experimental results show that the GI data link method proposed in this paper is obviously better than this method, which further proves the accuracy and effectiveness of the GI data link method proposed in this paper. 


Machine Learning, Geographic Information, Geographic Features, Linking Methods


Jun Shu, Geographic Information Linking Method Based on Machine Learning. Information and Knowledge Management (2022) Vol. 3: 23-29. DOI:


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