多源道路网数据自动匹配算法研究

项目名称： 多源道路网数据自动匹配算法研究

项目编号： No.41301424

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 天文学、地球科学

项目作者： 张猛

作者单位： 西安交通大学

项目金额： 25万元

中文摘要： 随着GIS技术的不断发展，一个不可避免的新课题摆在了我们面前，即"多源空间数据的匹配与融合"。作为其中的一个重要分支，"多源道路网数据自动匹配算法"的研究始于上个世纪90年代中期，至今已取得了许多成果，开发出诸如BG、ICP等匹配算法和模型。本课题将对传统BG、ICP算法的优缺点进行全面的研究分析，并拟在此基础上创建新的基于"网络要素"比对的匹配算法，该算法能够更加全面的考虑道路间的拓扑关系与上下文关系，从而期望在匹配的成功率和准确率方面能有所突破。同时，本课题将设法在匹配运算中引入"模式识别"和"地图综合"的新思维与新方法，以便使该算法更具普遍性与一般性。此外，本课题还将尝试使用"云计算技术"，为匹配海量道路网数据的快速运算，甚至是实时运算提供必要的技术支撑。简言之，本课题旨在研究新的理论算法，开发相应的软件产品，将"多源道路网数据自动匹配算法"的研究推向新的高度。

中文关键词： 数据匹配与融合；多源；多比例尺；道路网；

英文摘要： The growing demand on geospatial services requires an emphasized study on geo-information from various sources covering the same geographic space. Data matching, which aims at establishing logical connections (linkages) between corresponding objects or object parts in two comparable datasets, is one of the fundamental measures that helps make different datasets interoperable. A road network serves in many cases as the geometric and functional backbone in a comprehensive digital landscape model. Hence, street matching has been intensively and extensively researched during the last decade. Buffer Growing(BG) and Iterative Closest Point (ICP) are two popular matching algorithms that have been most frequently cited in literature so far. A majority of the existing matching approaches based on these two algorithms or their combination reveal a high matching rate and efficiency on certain data types of selected test areas. However, the problem of uncertain matching remains either in areas where the context is too complex or when one of the datasets contains little or no meaningful semantic information. Under the assumption that if more context information could be involved, the matching result would be better, this research is devoted to a new automatci matching algorithm based on network comparisons.Different to BG

英文关键词： data matching and conflation；multi-souces；multi-scales；road-network；