Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors
Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/11/457 |
| _version_ | 1827639735469735936 |
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| author | Ying Sun Yuefeng Lu Ziqi Ding Qiao Wen Jing Li Yanru Liu Kaizhong Yao |
| author_facet | Ying Sun Yuefeng Lu Ziqi Ding Qiao Wen Jing Li Yanru Liu Kaizhong Yao |
| author_sort | Ying Sun |
| collection | DOAJ |
| description | Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets. |
| first_indexed | 2024-03-09T16:47:16Z |
| format | Article |
| id | doaj.art-bff03464514f429a99a42c6f85075593 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T16:47:16Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-bff03464514f429a99a42c6f850755932023-11-24T14:45:31ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-11-01121145710.3390/ijgi12110457Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape DescriptorsYing Sun0Yuefeng Lu1Ziqi Ding2Qiao Wen3Jing Li4Yanru Liu5Kaizhong Yao6School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaChongqing Road Secondary School, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaMost commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets.https://www.mdpi.com/2220-9964/12/11/457SODshape descriptormulti-scale vector spatial datadata matchinglandmark extraction |
| spellingShingle | Ying Sun Yuefeng Lu Ziqi Ding Qiao Wen Jing Li Yanru Liu Kaizhong Yao Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors ISPRS International Journal of Geo-Information SOD shape descriptor multi-scale vector spatial data data matching landmark extraction |
| title | Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors |
| title_full | Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors |
| title_fullStr | Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors |
| title_full_unstemmed | Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors |
| title_short | Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors |
| title_sort | multi scale road matching based on the summation product of orientation and distance and shape descriptors |
| topic | SOD shape descriptor multi-scale vector spatial data data matching landmark extraction |
| url | https://www.mdpi.com/2220-9964/12/11/457 |
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