Underground Pipeline Data Matching Considering Multiple Spatial Similarities

doi:10.11947/j.AGCS.2015.20150207

Abstract

Abstract: Integrated and professional underground pipeline data are two forms of pipeline.Integrated underground pipeline data is accurate and general, while professional underground pipeline data expresses and contains detailed attribute information.Taking the data of natural gas pipeline as an example, this paper calculates structural similarity measured by the distribution pattern of pipelines that pipeline-point connects with, semantic similarity presented by the names and attributes of the pipeline-point ontology concept, and shape similarity characterized by the shape of arcs between two pipeline-points. The matching of pipe points is realized with the method of support vector machine classification algorithm and unique-matching principle combined with these spatial similarity. Test results show the matching of pipe points is well solved by the proposed algorithm.

Key words: integrated pipeline, professional pipeline, data matching, spatial similarity, support vector machine

CLC Number:

P208

GONG Minxia, YUAN Sai, CHU Zhengwei, ZHANG Shuliang, FANG Caili. Underground Pipeline Data Matching Considering Multiple Spatial Similarities[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12): 1392-1400.

References

[1] ZHANG Shuliang, CHU Zhengwei, HE Yuan, et al. The Analysis of the Differences of Spatial Data on Urban Integrated and Professional Underground Pipeline[J]. Bulletin of Surveying and Mapping, 2013(12): 58-62. (张书亮, 储征伟, 何源, 等. 城市综合与专业地下管线空间数据的差异性分析[J]. 测绘通报, 2013(12): 58-62.)
[2] HAO Yanling, TANG Wenjing, ZHAO Yuxin, et al. Areal Feature Matching Algorithm Based on Spatial Similarity[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(4): 501-506. (郝燕玲, 唐文静, 赵玉新, 等. 基于空间相似性的面实体匹配算法研究[J]. 测绘学报, 2008, 37(4): 501-506.)
[3] COBB M A, CHUNG M J, FOLEY III H, et al. A Rule-Based Approach for the Conflation of Attributed Vector Data[J]. GeoInformatica, 1998, 2(1): 7-35.
[4] HU Yungang, CHEN Jun, ZHAO Renliang, et al. Matching of Roads under Different Scales for Updating Map Data[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4): 451-456. (胡云岗, 陈军, 赵仁亮, 等. 地图数据缩编更新中道路数据匹配方法[J]. 武汉大学学报:信息科学版, 2010, 35(4): 451-456.)
[5] WALTER V, FRITSCH D. Matching Spatial Data Sets: A Statistical Approach[J]. International Journal of Geographical Information Science, 1999, 13(5): 445-473.
[6] MUSTIÈRE S, DEVOGELE T. Matching Networks with Different Levels of Detail[J]. GeoInformatica, 2008, 12(4): 435-453.
[7] CHEN Yumin, GONG Jianya, SHI Wenzhong.A Distance-based Matching Algorithm for Multi-scale Road Networks[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(1): 84-90. (陈玉敏, 龚健雅, 史文中. 多尺度道路网的距离匹配算法研究[J]. 测绘学报, 2007, 36(1): 84-90.)
[8] LUO Lei, YIN Jianping, ZHANG Guomin, et al. Point Matching for Shapes Based on Weighted Neighborhood Relationships[J]. Computer Engineering & Science, 2008, 30(11): 34-37. (罗磊, 殷建平, 张国敏, 等. 基于加权相邻关系的形状轮廓点匹配[J]. 计算机工程与科学, 2008, 30(11): 34-37.)
[9] RODRÍGUEZ M A, EGENHOFER M J. Determining Semantic Similarity among Entity Classes from Different Ontologies[J]. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(2): 442-456.
[10] LUAN Xuechen, YANG Bisheng, LI Qiuping. Pattern-based Node Matching Approach for Road Networks[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4): 608-614. (栾学晨, 杨必胜, 李秋萍. 基于结构模式的道路网节点匹配方法[J]. 测绘学报, 2013, 42(4): 608-614.)
[11] ZHAO Dongbao, SHENG Yehua. Research on Automatic Matching of Vector Road Networks Based on Global Optimization[J].Acta Geodaetica et Cartographica Sinica, 2010, 39(4): 416-421. (赵东保, 盛业华. 全局寻优的矢量道路网自动匹配方法研究[J]. 测绘学报, 2010, 39(4): 416-421.)
[12] ZHANG Yunfei, YANG Bisheng, LUAN Xuechen. Automated Matching Urban Road Networks Using Probabilistic Relaxation[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6): 933-939. (张云菲, 杨必胜, 栾学晨. 利用概率松弛法的城市路网自动匹配[J]. 测绘学报, 2012, 41(6): 933-939.)
[13] GONG Xianyong, WU Fang, JI Cunwei, et al. Ant Colony Optimization Approach to Road Network Matching[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 191-195. (巩现勇, 武芳, 姬存伟, 等. 道路网匹配的蚁群算法求解模型[J]. 武汉大学学报:信息科学版, 2014, 39(2): 191-195.)
[14] YANG Jieming, TANG Dingfu, ZHANG Minglan, et al. GB/T 28590-2012, Classification and Code for Urban Underground Facilities[S]. Beijing: China Standard Publishing House, 2012.(杨洁明, 唐定富, 张明兰, 等. GB/T 28590-2012, 城市地下空间设施分类与代码[S].北京: 中国标准出版社, 2012.)
[15] HONG Libo, ZHOU Fenglin, QU Fubang, et al. CJJ61-2003,Technical Specification for Detecting and Surveying Underground Pipelines and Cables in City[S].Beijing: China Building Industry Press, 2004.(洪立波, 周凤林, 曲福邦, 等. CJJ 61-2003, 城市地下管线探测技术规程[S].北京: 中国建筑工业出版社, 2004.)
[16] NJCH002, Technical Specification for Detecting and Surveying Pipelines and Cables in Nanjing[S]. Nanjing: Nanjing Urban Planning Bureau, 2004. (NJCH002, 南京市管线探测技术规程[S].南京:南京市规划局,2014.)
[17] TVERSKY A. Features of Similarity[J]. Psychological Review, 1977, 84(4): 327-352.
[18] YAN Weiwu, SHAO Huihe. Application of Support Vector Machines and Least Squares Support Vector Machines to Heart Disease Diagnoses[J]. Control and Decision, 2003, 18(3): 358-360. (阎威武, 邵惠鹤. 支持向量机和最小二乘支持向量机的比较及应用研究[J]. 控制与决策, 2003, 18(3): 358-360.)
[19] DINGShifei, QI Bingjuan, TAN Hongyan. An Overview on Theory and Algorithm of Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. (丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1): 2-10.)
[20] VIERA A J, GARRETT J M. Understanding Interobserver Agreement: The Kappa Statistic[J]. Family Medicine, 2005, 37(5): 360-363.