顾及多空间相似性的地下管线数据匹配

龚敏霞; 袁赛; 储征伟; 张书亮; 房彩丽

doi:10.11947/j.AGCS.2015.20150207

测绘学报 >

2015 , Vol. 44 >Issue 12: 1392 - 1400

DOI: https://doi.org/10.11947/j.AGCS.2015.20150207

地理学与地理信息

顾及多空间相似性的地下管线数据匹配

龚敏霞 ,
袁赛 ,
储征伟 ,
张书亮 ,
房彩丽

展开

1. 南京师范大学虚拟地理环境教育部重点实验室, 江苏南京 210023;
2. 江苏省国土资源信息中心, 江苏南京 210017;
3. 南京师范大学强化培养学院, 江苏南京 210023;
4. 南京市测绘勘察研究院有限公司, 江苏南京 210019;
5. 江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023

龚敏霞(1976-),女,博士生,主要研究方向为空间相似性。E-mail:gmx@jsmlr.gov.cn

收稿日期: 2015-04-20

修回日期: 2015-10-15

网络出版日期: 2016-01-04

基金资助

国家自然科学基金(41171301);江苏高校优势学科建设工程资助项目

收起

Underground Pipeline Data Matching Considering Multiple Spatial Similarities

GONG Minxia ,
YUAN Sai ,
CHU Zhengwei ,
ZHANG Shuliang ,
FANG Caili

Expand

1. Key Laboratory of Virtual Geographic Environment for the Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
2. Information Center of Jiangsu of Land and Resources, Nanjing 210017, China;
3. Honour School of Nanjing Normal University, Nanjing 210023, China;
4. Nanjing Institute of Surveying, Mapping and Geotechnical Investigation, Co. Ltd, Nanjing 210019, China;
5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received date: 2015-04-20

Revised date: 2015-10-15

Online published: 2016-01-04

Supported by

The National Natural Science Foundation of China (No.41171301);A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

Fold

摘要

综合地下管线与专业地下管线是同一管线地物的两种表达形式。综合地下管线数据精准、概括;专业地下管线数据表达范围广、属性信息翔实。本文以天然气管线数据为例,选取管点关联管段分布形态作为衡量依据计算管线结构相似性,从管点本体概念名称和属性两方面计算管线语义相似性,以两管点间管段分布形态为特征计算管线形状相似性。以此空间相似性构建管点特征向量,采用SVM(support vector machine)支持向量机的分类方法及管点唯一匹配原则实现管点实体匹配。试验表明该算法能够有效解决管点匹配问题。

关键词： 综合管线; 专业管线; 数据匹配; 空间相似性; SVM支持向量机

本文引用格式

龚敏霞 , 袁赛 , 储征伟 , 张书亮 , 房彩丽 . 顾及多空间相似性的地下管线数据匹配[J]. 测绘学报, 2015 , 44(12) : 1392 -1400 . DOI: 10.11947/j.AGCS.2015.20150207

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

参考文献

[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.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献