Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor
In geographic information data processing, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching. Spatial relationship is the key to matching because it remains stable at different scales. In this pape...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/7/280 |
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| author | Yu Yan Ying Sun Shaobo Wang Yuefeng Lu Yulong Hu Miao Lu |
| author_facet | Yu Yan Ying Sun Shaobo Wang Yuefeng Lu Yulong Hu Miao Lu |
| author_sort | Yu Yan |
| collection | DOAJ |
| description | In geographic information data processing, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching. Spatial relationship is the key to matching because it remains stable at different scales. In this paper, we propose an improved summation product of direction and distance (ISOD) descriptor, which combines features such as included angle chain and camber variance with similarity features such as length, direction, and Hausdorff distance to construct an integrated similarity metric model for multi-scale road matching. The experiments proved that the model achieved 94.75% and 93.34% precision and recall in 1:50,000 and 1:10,000 scale road data matching and 86.39% and 94.06% in 1:250,000 and 1:50,000 scale road data matching, respectively. This proves the effectiveness and practicality of the method. The ISOD descriptor and integrated similarity metric model in this paper provide an effective method for multi-scale road data matching, which helps the integration and fusion of geographic information data, and has an important application value in the fields of intelligent transport and urban planning. |
| format | Article |
| id | doaj-art-1c404d396e4242dabd5e7100b4dc2cc4 |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-1c404d396e4242dabd5e7100b4dc2cc42025-08-20T03:58:29ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-07-0114728010.3390/ijgi14070280Research on Multi-Scale Vector Road-Matching Model Based on ISOD DescriptorYu Yan0Ying Sun1Shaobo Wang2Yuefeng Lu3Yulong Hu4Miao Lu5School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaZibo Natural Resources and Planning Bureau Economic Development Zone Branch, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, ChinaGuojiao Spatial Information Technology (Beijing) Co., Ltd., Beijing 100011, ChinaNational Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257347, ChinaIn geographic information data processing, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching. Spatial relationship is the key to matching because it remains stable at different scales. In this paper, we propose an improved summation product of direction and distance (ISOD) descriptor, which combines features such as included angle chain and camber variance with similarity features such as length, direction, and Hausdorff distance to construct an integrated similarity metric model for multi-scale road matching. The experiments proved that the model achieved 94.75% and 93.34% precision and recall in 1:50,000 and 1:10,000 scale road data matching and 86.39% and 94.06% in 1:250,000 and 1:50,000 scale road data matching, respectively. This proves the effectiveness and practicality of the method. The ISOD descriptor and integrated similarity metric model in this paper provide an effective method for multi-scale road data matching, which helps the integration and fusion of geographic information data, and has an important application value in the fields of intelligent transport and urban planning.https://www.mdpi.com/2220-9964/14/7/280spatial relationshipsISOD descriptorincluded angle chaincambervariancemulti-scale road networks |
| spellingShingle | Yu Yan Ying Sun Shaobo Wang Yuefeng Lu Yulong Hu Miao Lu Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor ISPRS International Journal of Geo-Information spatial relationships ISOD descriptor included angle chain cambervariance multi-scale road networks |
| title | Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor |
| title_full | Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor |
| title_fullStr | Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor |
| title_full_unstemmed | Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor |
| title_short | Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor |
| title_sort | research on multi scale vector road matching model based on isod descriptor |
| topic | spatial relationships ISOD descriptor included angle chain cambervariance multi-scale road networks |
| url | https://www.mdpi.com/2220-9964/14/7/280 |
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