众源矢量数据分块纠正研究

Research on Block Correction of Crowd Sourcing Vector Data

众源矢量数据能够提供丰富的地理信息,但存在几何误差分布不均匀问题。针对众源矢量数据几何误差问题,本文提出一种众源矢量数据分块纠正方法。以遥感影像作为标准数据,通过模板匹配得到遥感影像与众源矢量数据的同名点,根据同名点的几何误差分布对众源矢量数据切分及分块纠正,最后拼接纠正结果实现众源矢量数据的纠正。本文以上海市OpenStreetMap矢量道路网作为实验数据进行分块纠正实验,实验结果表明分块纠正后众源矢量数据几何误差显著降低。

Crowd sourcing vector data provides rich geographic information,but there is a problem of uneven geometric error distribution.Aiming at the geometric error of crowd sourcing vector data,this paper proposes a block correction method for crowd sourcing vector data.Using remote sensing image as standard data,the same-named point of remote sensing image and crowd sourcing vector data is obtained through template matching.The crowd sourcing vector data is segmented and corrected according to the geometric error distribution of the same-named point,and finally Stitching correction results to achieve correction of crowd sourcing vector data.In this paper,the Shanghai OpenStreetMap vector road network is used as experimental data to carry out block correction experiments.The experimental results show that the geometric errors of the crowd source vector data are significantly reduced after block correction.