测绘学报 ›› 2017, Vol. 46 ›› Issue (10): 1627-1636.doi: 10.11947/j.AGCS.2017.20170387

• 地图学与地理信息 • 上一篇    下一篇

多源矢量空间数据融合处理技术研究进展

孙群   

  1. 信息工程大学地理空间信息学院, 河南 郑州 450052
  • 收稿日期:2017-07-26 修回日期:2017-09-11 出版日期:2017-10-20 发布日期:2017-10-26
  • 作者简介:孙群(1963-),男,博士,教授,博士生导师,研究方向为数字地图制图与地理信息处理。
  • 基金资助:
    国家自然科学基金(41571399)

Research on the Progress of Multi-sources Geospatial Vector Data Fusion

SUN Qun   

  1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450052, China
  • Received:2017-07-26 Revised:2017-09-11 Online:2017-10-20 Published:2017-10-26
  • Supported by:
    The National Natural Science Foundation of China (No. 41571399)

摘要: 矢量空间数据既是人类社会与地理环境信息的重要组成部分,也是相关社会信息的重要载体,在国民经济和国防现代化建设中起着非常重要的作用。多源矢量空间数据融合处理技术是解决多源数据在几何位置、属性特征等方面不一致性问题的有效方法,近年来相关的技术和应用得到了深入发展。本文在分析二维矢量空间数据应用所面临问题的基础上,综述和评价了二维矢量空间数据几何特征融合、属性特征融合等相关理论、算法和技术的研究现状,并根据目前的研究展望了其理论和应用未来的重点研究方向。

关键词: 矢量空间数据, 同名实体匹配, 属性特征, 数据融合

Abstract: Geospatial vector data plays a crucial role in the national economy and the construction of the national defense modernization for it's not only the important component of human social and geographical environment information, but also a key carrier of relevant social information. The technology of the multi-source geospatial vector data fusion is a valid method of solving the inconsistency questions of the multi-source data in geometric position, attribute feature, etc. In recent years, its relevant technology as well as its application also has deeply developed. Based on the analysis of the questions in the application of the two-dimensional geospatial vector data are facing, the research status of the theory, algorithm and technologies of geometric feature fusion and attribute feature fusion of the two-dimensional geospatial vector data are overviewed and evaluated, with the current research status, whose theory and application of the future focus of research are looked forward to in this paper.

Key words: geospatial vector data, identical entity matching, attribute feature, data fusion

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