POI Data Fusion Method Based on Multi-Feature Matching and Optimization
The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is propose...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/1/26 |
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| author | Yue Wang Cailin Li Hongjun Zhang Baoyun Guo Xianlong Wei Zhao Hai |
| author_facet | Yue Wang Cailin Li Hongjun Zhang Baoyun Guo Xianlong Wei Zhao Hai |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. Firstly, when dealing with POI (point of interest) data, the similarity of POI data in terms of name, address, and distance is calculated by combining the improved hybrid similarity method, the Jaccard method, and the Euclidean metric method. Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. Finally, taking the area within the Second Ring Road in Beijing as the experimental area, the POI data of Tencent Maps and Amap are collected to verify the method proposed in this paper. The experimental results show that, compared with the existing POI matching methods, the accuracy and recall rate of the results obtained by the POI matching and fusion method proposed in this paper are significantly improved, which verifies the accuracy and feasibility of the matching. |
| format | Article |
| id | doaj-art-e536f3cdebf24d3eba600dd6fda2be5e |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-e536f3cdebf24d3eba600dd6fda2be5e2025-01-24T13:35:01ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-01-011412610.3390/ijgi14010026POI Data Fusion Method Based on Multi-Feature Matching and OptimizationYue Wang0Cailin Li1Hongjun Zhang2Baoyun Guo3Xianlong Wei4Zhao Hai5School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaGeographic Information Engineering, Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaThe key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. Firstly, when dealing with POI (point of interest) data, the similarity of POI data in terms of name, address, and distance is calculated by combining the improved hybrid similarity method, the Jaccard method, and the Euclidean metric method. Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. Finally, taking the area within the Second Ring Road in Beijing as the experimental area, the POI data of Tencent Maps and Amap are collected to verify the method proposed in this paper. The experimental results show that, compared with the existing POI matching methods, the accuracy and recall rate of the results obtained by the POI matching and fusion method proposed in this paper are significantly improved, which verifies the accuracy and feasibility of the matching.https://www.mdpi.com/2220-9964/14/1/26POIentity matchingspatial data integrationmatching accuracymachine learning |
| spellingShingle | Yue Wang Cailin Li Hongjun Zhang Baoyun Guo Xianlong Wei Zhao Hai POI Data Fusion Method Based on Multi-Feature Matching and Optimization ISPRS International Journal of Geo-Information POI entity matching spatial data integration matching accuracy machine learning |
| title | POI Data Fusion Method Based on Multi-Feature Matching and Optimization |
| title_full | POI Data Fusion Method Based on Multi-Feature Matching and Optimization |
| title_fullStr | POI Data Fusion Method Based on Multi-Feature Matching and Optimization |
| title_full_unstemmed | POI Data Fusion Method Based on Multi-Feature Matching and Optimization |
| title_short | POI Data Fusion Method Based on Multi-Feature Matching and Optimization |
| title_sort | poi data fusion method based on multi feature matching and optimization |
| topic | POI entity matching spatial data integration matching accuracy machine learning |
| url | https://www.mdpi.com/2220-9964/14/1/26 |
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