张博, 张猛, 王非, 范红超. VGI数据与地形图数据的自动融合研究[J]. 武汉大学学报 ( 信息科学版), 2019, 44(11): 1708-1714. DOI: 10.13203/j.whugis20180023
引用本文: 张博, 张猛, 王非, 范红超. VGI数据与地形图数据的自动融合研究[J]. 武汉大学学报 ( 信息科学版), 2019, 44(11): 1708-1714. DOI: 10.13203/j.whugis20180023
ZHANG Bo, ZHANG Meng, WANG Fei, FAN Hongchao. Automatic Data Integration between VGI and Topographic Data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1708-1714. DOI: 10.13203/j.whugis20180023
Citation: ZHANG Bo, ZHANG Meng, WANG Fei, FAN Hongchao. Automatic Data Integration between VGI and Topographic Data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1708-1714. DOI: 10.13203/j.whugis20180023

VGI数据与地形图数据的自动融合研究

Automatic Data Integration between VGI and Topographic Data

  • 摘要: 随着科学技术的不断发展,志愿者地理信息(volunteered geographic information,VGI)已经成为地理空间数据中最为重要的来源之一。为了充分利用志愿者地理信息,需要进行VGI与传统地形图数据的匹配与融合。开发了一种全新的数据自动匹配与融合算法,其目的是将ATKIS道路网数据(由德国联邦测绘局所采集的官方数据)与AOSD数据(由大量志愿者携带定位仪器进行户外徒步或骑行所获取的轨迹数据)匹配并融合起来,从而丰富传统地理信息数据的内容,并实现数据的增值。考虑到ATKIS数据与AOSD数据在空间表达上的差异很大,所开发的算法包括了道路要素的智能化分割、道路要素匹配、道路网数据融合以及融合后道路网内部要素间的匹配运算与数据集成等4个过程。大量实地数据的测试结果表明,该算法具有匹配成功率高、准确率高、运算速度快等优点。

     

    Abstract: With the continuous development of science and technology, volunteer geographic information (VGI) has become one of the most important data sources in geographic data acquisition. In order to make more efficient use of such volunteered data, the VGI is often needed to be integrated to the corresponding traditional datasets. We develop a special case of data integration between the topographic dataset of ATKIS maintained by German Surveying and Mapping Agencies and the AOSD data primarily collected by numerous enthusiasts (volunteers) of jogging, hiking, biking (including road and mountain), etc. Considering that the VGI of AOSD reveals quite different LODs (level of details) to the traditional topographic dataset of ATKIS, we put forward a new approach to realize highly automatic and accurate integrations of these two datasets. The proposed approach is characterized by 4 processes:(1) intelligent segmentation of the road features, (2) road-networks matching between different datasets, (3) data integration between different road networks and (4) internal data matching and integration in the conflated road network. Experimental results demonstrate high performance with respect to matching rate, matching accuracy and computing speed in a number of large test areas.

     

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