志愿者地理信息中天桥的自动识别方法

doi:10.11947/j.AGCS.2017.20160070

测绘学报 ›› 2017, Vol. 46 ›› Issue (2): 246-252.doi: 10.11947/j.AGCS.2017.20160070

志愿者地理信息中天桥的自动识别方法

马超¹, 孙群¹, 陈换新², 徐青¹, 杨辉³

1. 信息工程大学地理空间信息学院, 河南郑州 450000;
2. 96633部队, 北京 100096;
3. 69027部队, 新疆乌鲁木齐 830002

收稿日期:2016-02-22 修回日期:2016-11-16 出版日期:2017-02-20 发布日期:2017-03-07
作者简介:马超(1988-),男,博士生,主要从事多源空间数据融合处理与数字地图制图研究。E-mail:jielong018@126.com
基金资助:
国家863项目（2012AA12A404）；国家自然科学基金（41571399；41201391；41071297；41201469）

The Recognition of Overpass in Volunteered Geographic Information

MA Chao¹, SUN Qun¹, CHEN Huanxin², XU Qing¹, YANG Hui³

1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China;
2. Troops 96633, Beijing 100096, China;
3. Troops 69027, Urumqi 830002, China

Received:2016-02-22 Revised:2016-11-16 Online:2017-02-20 Published:2017-03-07
Contact: 孙群 E-mail:sunqun@371.net
Supported by:
The National High Technology Research and Development Program of China(863 Program)(No. 2012AA12A404),The National Natural Science Foundation of China(Nos.41571399,41201391,41071297,41201469)

摘要/Abstract

摘要： 基于天桥的几何与属性特征，提出了一种志愿者地理信息中自动识别天桥的方法。天桥从几何结构上可分为主桥和附属设施两个部分，主桥部分特征鲜明，可以视为两类分类问题，依据其几何特征和属性特征构建特征空间，利用支持向量机的方法进行识别；附属设施部分可依据已识别的天桥主桥，按照路段的长度、属性等判定规则进行识别，从而完成整个天桥的自动识别。以北京市OpenStreetMap（osm）数据进行试验验证的结果表明，本文提出的方法能有效地识别出志愿者地理信息中的典型天桥结构，可以为志愿者地理信息道路网的多尺度建模与化简、步行导航等提供帮助。

关键词: 天桥, 结构识别, 支持向量机, 志愿者地理信息

Abstract: The paper presents an overpass recognition method in volunteered geographic information based on the geometry and attribute characteristics. The structure of the overpass is divided into the main bridge parts and the affiliated facilities. The main bridge parts with distinctive characters could be treated as a two-class classification problem. The characteristic vectors could build on the foundation of analysis and quantization the geometry and attribute characteristics. Then, the main bridge is recognized automatically through the support vector machine. The affiliated facilities of the overpass are recognized based on the main bridge with some relevant judgment rules. The OpenStreetMap(osm) is selected for the experiment. The results show that the method could effectively recognize the overpass and could provide help for the road simplification and walking guidance.

Key words: overpass, structure recognition, support vector machine, volunteered geographic information

中图分类号:

P208

马超, 孙群, 陈换新, 徐青, 杨辉. 志愿者地理信息中天桥的自动识别方法[J]. 测绘学报, 2017, 46(2): 246-252.

MA Chao, SUN Qun, CHEN Huanxin, XU Qing, YANG Hui. The Recognition of Overpass in Volunteered Geographic Information[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(2): 246-252.

参考文献

[1] GOODCHILD M F. Citizens as Sensors:The World of Volunteered Geography[J]. GeoJournal, 2007, 69(4):211-221.
[2] SUI D, GOODCHILD M F. The Convergence of GIS and Social Media:Challenges for GIScience[J]. International Journal of Geographical Information Science, 2011, 25(11):1737-1748.
[3] 张红平, 顾学云, 熊萍, 等. 志愿者地理信息研究与应用初探[J]. 地理信息世界, 2012, 8(4):67-71. ZHANG Hongping, GU Xueyun, XIONG Ping, et al. Development and Application of Volunteered Geographic Information[J]. Geomatics World, 2012, 8(4):67-71.
[4] 单杰, 秦坤, 黄长青, 等. 众源地理数据处理与分析方法探讨[J]. 武汉大学学报(信息科学版), 2014, 39(4):390-396. SHAN Jie,QIN Kun,HUANG Changqing, et al. Methods of Crowd Sourcing Geographic Data Processing and Analysis[J]. Geomatics and Information Science of Wuhan University, 2014, 39(4):390-396.
[5] HAKLAY M. How Good is Volunteered Geographical Information? A Comparative Study of Open Street Map and Ordnance Survey datasets[J]. Environment and Planning B:Planning and Design, 2010, 37(4):682-703.
[6] FORGHANI M, DELAVAR M R. A Quality Study of the Open Street Map Dataset for Tehran[J]. ISPRS International Journal of Geo-Information, 2014, 3(2):750-763.
[7] FLANAGIN A J, METZGER M J. The Credibility of Volunteered Geographic Information[J]. GeoJournal, 2008, 72(3-4):137-148.
[8] 赵肄江, 周晓光. 地理信息志愿者信誉度评估的版本相似度模型——以面目标为例[J]. 测绘学报, 2015, 44(5):578-584. DOI:10.11947/j.AGCS.2015.20140065. ZHAO Yijiang, ZHOU Xiaoguang. Version Similarity-based Model for Volunteers' Reputation of Volunteered Geographic Information:A Case Study of Polygon[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5):578-584. DOI:10.11947/j.AGCS.2015.20140065.
[9] 蒙艳姿. 道路交叉口的结构化识别与简化[D]. 成都:西南交通大学, 2009:54-67. MENG Yanzi. Structural Pattern Recognition and Simplification of Road Junctions[D]. Chengdu:Southwest Jiaotong University, 2009.
[10] YANG Bisheng, LUAN Xuechen, LI Qingquan. An Adaptive Method for Identifying the Spatial Patterns in Road Nerworks[J]. Computers, Environment and Urban Systems, 2010, 34(1):40-48.
[11] 翟仁建. 基于全局一致性评价的多尺度矢量空间数据匹配方法研究[D]. 郑州:信息工程大学, 2011. ZHAI Renjian. Research on Automated Matching Methods for Multi-scale Vector Spatial Data Based on Global Consistency Evaluation[D]. Zhengzhou:Information Engineering University, 2011.
[12] 栾学晨. 保持结构模式的道路网数据多尺度建模[D]. 武汉:武汉大学, 2013. LUAN Xuechen. Methods for Modeling Levels-of-detail Road Networks with the Maintenance of Structural Patterns[D]. Wuhan:Wuhan University, 2013.
[13] 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. 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.
[14] MACKANESS W A, MACKECHNIE G A. Automating the Detection and Simplification of Junctions in Road Networks[J]. GeoInformatica, 1999, 3(2):185-200.
[15] 徐柱, 蒙艳姿, 李志林, 等. 基于有向属性关系图的典型道路交叉口结构识别方法[J]. 测绘学报, 2011, 40(1):125-131. XU Zhu, MENG Yanzi, LI Zhilin, et al. Recognition of Structures of Typical Road Junctions Based on Directed Attributed Relational Graph[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(1):125-131.
[16] SCHEIDER S, POSSIN J. Affordance-based Individuation of Junctions in Open Street Map[J]. Journal of Spatial Information Science, 2012, 4(3):31-56.
[17] 王骁, 钱海忠, 丁雅莉, 等. 采用拓扑关系与道路分类的立交桥整体识别方法[J]. 测绘科学技术学报, 2013, 30(3):324-328. WANG Xiao, QIAN Haizhong, DING Yali, et al. The Integral Identification Method of Cloverleaf Junction Based on Topology and Road Classification[J]. Journal of Geomatics Science and Technology, 2013, 30(3):324-328.
[18] 栾学晨, 范红超, 杨必胜, 等. 城市道路网主干道提取的形态分析方法[J]. 武汉大学学报(信息科学版), 2014, 39(3):327-331. LUAN Xuechen, FAN Hongchao, YANG Bisheng, et al. Arterial Roads Extraction in Urban Road Networks Based on Shape Analysis[J]. Geomatics and Information Science of Wuhan University, 2014, 39(3):327-331.
[19] 邓乃扬, 田英杰. 支持向量机-理论、算法与拓展[M]. 北京:科学出版社, 2009. DENG Naiyang,TIAN Yingjie.Support Vector Matchines-Theory, Algorithm and Extention[M]. Beijing:Science Press, 2009.
[20] 胡俊. 支持向量机参数优化问题的研究[D]. 哈尔滨:哈尔滨工业大学, 2009. HU Jun. Research on Parameter Optimization of Support Vector Machine[D]. Harbin:Harbin Institute of Technology, 2009.
[21] 马超, 孙群, 陈换新, 等. 利用路段分类识别复杂道路交叉口[J]. 武汉大学学报(信息科学版), 2016, 41(9):1232-1237. MA Chao, SUN Qun, CHEN Huanxin, et al. Recognition of Road Junctions Based on Road Classification Method[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9):1232-1237.

志愿者地理信息中天桥的自动识别方法

The Recognition of Overpass in Volunteered Geographic Information

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