Pattern Recognition in Urban Maps Based on Graph Structures
Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/5/191 |
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| _version_ | 1850257846666526720 |
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| author | Xiaomin Lu Zhiyi Zhang Haoran Song Haowen Yan |
| author_facet | Xiaomin Lu Zhiyi Zhang Haoran Song Haowen Yan |
| author_sort | Xiaomin Lu |
| collection | DOAJ |
| description | Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both have achieved certain recognition outcomes, they suffer from four key limitations: (1) insufficient algorithmic interpretability; (2) limited model generalizability; (3) restricted pattern diversity in recognition; (4) inability of existing methods (including deep learning and traditional algorithms) to achieve multi-pattern recognition across heterogeneous map group types (e.g., building groups vs. road networks) using a single framework. To address these limitations, this study proposes a graph structure-based multi-pattern recognition algorithm for map groups. The algorithm integrates the quantitative advantages of directional entropy in characterizing spatial distribution patterns with the discriminative power of node degree in analyzing edge-node geometric models. Experimental validation utilized building and road network data from multiple cities, constructing a dataset of 600 samples divided into two subsets: Sample Set 1 (for parameter threshold calibration and rule generation) and Sample Set 2 (for algorithm performance validation and transferability testing). The results demonstrate a classification accuracy of 97% for the proposed algorithm, effectively distinguishing four building group patterns (linear, curved, grid, irregular) and two road network patterns (grid, irregular). This work establishes a novel methodological framework for multi-scale spatial pattern analysis in map generalization and urban planning. |
| format | Article |
| id | doaj-art-9a4c7455629842e986843cff0eb6dc32 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-9a4c7455629842e986843cff0eb6dc322025-08-20T01:56:19ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114519110.3390/ijgi14050191Pattern Recognition in Urban Maps Based on Graph StructuresXiaomin Lu0Zhiyi Zhang1Haoran Song2Haowen Yan3Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaMap groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both have achieved certain recognition outcomes, they suffer from four key limitations: (1) insufficient algorithmic interpretability; (2) limited model generalizability; (3) restricted pattern diversity in recognition; (4) inability of existing methods (including deep learning and traditional algorithms) to achieve multi-pattern recognition across heterogeneous map group types (e.g., building groups vs. road networks) using a single framework. To address these limitations, this study proposes a graph structure-based multi-pattern recognition algorithm for map groups. The algorithm integrates the quantitative advantages of directional entropy in characterizing spatial distribution patterns with the discriminative power of node degree in analyzing edge-node geometric models. Experimental validation utilized building and road network data from multiple cities, constructing a dataset of 600 samples divided into two subsets: Sample Set 1 (for parameter threshold calibration and rule generation) and Sample Set 2 (for algorithm performance validation and transferability testing). The results demonstrate a classification accuracy of 97% for the proposed algorithm, effectively distinguishing four building group patterns (linear, curved, grid, irregular) and two road network patterns (grid, irregular). This work establishes a novel methodological framework for multi-scale spatial pattern analysis in map generalization and urban planning.https://www.mdpi.com/2220-9964/14/5/191graph structuremap groupspattern recognitiondirection entropynode degree |
| spellingShingle | Xiaomin Lu Zhiyi Zhang Haoran Song Haowen Yan Pattern Recognition in Urban Maps Based on Graph Structures ISPRS International Journal of Geo-Information graph structure map groups pattern recognition direction entropy node degree |
| title | Pattern Recognition in Urban Maps Based on Graph Structures |
| title_full | Pattern Recognition in Urban Maps Based on Graph Structures |
| title_fullStr | Pattern Recognition in Urban Maps Based on Graph Structures |
| title_full_unstemmed | Pattern Recognition in Urban Maps Based on Graph Structures |
| title_short | Pattern Recognition in Urban Maps Based on Graph Structures |
| title_sort | pattern recognition in urban maps based on graph structures |
| topic | graph structure map groups pattern recognition direction entropy node degree |
| url | https://www.mdpi.com/2220-9964/14/5/191 |
| work_keys_str_mv | AT xiaominlu patternrecognitioninurbanmapsbasedongraphstructures AT zhiyizhang patternrecognitioninurbanmapsbasedongraphstructures AT haoransong patternrecognitioninurbanmapsbasedongraphstructures AT haowenyan patternrecognitioninurbanmapsbasedongraphstructures |