基于机器学习的地理空间数据抽取算法

doi:10.13229/j.cnki.jdxbgxb20200113

Abstract

Abstract:

In order to improve the accuracy and recall of geospatial data integration extraction， a geospatial data extraction algorithm based on machine learning is proposed. GeoNames， OpenStreetMap， etc. are used as the data sources of geographic information. Through web crawler and search engine， the relevant web pages are searched and downloaded at the same time， and the content is filtered. After filtering， the location name and address information and other data in the web pages are parsed and extracted to realize visualization. By analyzing and extracting the geographic data entities， using the mapping between geographic data and entities， the disambiguation of heterogeneous geographic data is eliminated， the integration of geospatial data is realized， and the digital features of geographic data are realized according to the similarity degree calculation of multi features such as entity name and category. Combined with multi feature and machine learning KNN classification method， the proposed algorithm can complete the automation of geographic data link and realize the classification and extraction of geospatial data. The experimental results show that the proposed algorithm has high precision and recall， and the data extraction effect is good， which can lay a foundation for the integrated extraction of geographic data.

Key words: computer application, machine learning, geospatial data, extraction algorithm

CLC Number:

P208

Xiao-long ZHU,Zhong XIE. Geospatial data extraction algorithm based on machine learning[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1011-1016.

Figures/Tables 5

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