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

Automatic Data Integration between VGI and Topographic Data

Funds: 

The National Natural Science Foundation of China 41871315

the Project of Scientific Research and Development of Urban and Rural Construction in Shaanxi 2015-K116

the Fundamental Research Funds for the Central Universities xjj2016041

More Information
  • Author Bio:

    ZHANG Bo, PhD, specializes in automatic matching algorithm for multi-source and multi-scale road network data. E-mail:18681888878@163.com

  • Corresponding author:

    ZHANG Meng, PhD, associate professor. E-mail: sql_zhang@hotmail.com

  • Received Date: May 23, 2018
  • Published Date: November 04, 2019
  • 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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