A new approach for matching road lines using efficiency rates of similarity measures

IF 3.1 Q2 ENGINEERING, GEOLOGICAL International Journal of Engineering and Geosciences Pub Date : 2020-11-04 DOI:10.26833/IJEG.791324
M. Hacar, T. Gökgöz
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引用次数: 0

Abstract

The lack of common semantic information among corresponding geo-objects in different datasets required new matching approaches based on geometric and topological measures. In this study, a semi-automated matching approach based on the matching capabilities of geometric and topological measures was proposed. In the first stage, after the initial matching performed by a scoring system, the efficiency of each measure on the matching accuracy is evaluated manually by an operator. In the second stage, (1) the score of each measure is updated in accordance with the accuracy distributions. This means that the score of a measure is increased if it is relatively more significant than others. Finally, (2) matching process is repeated with new scores. The proposed approach was tested by matching tree-, cellular-, and hybrid-patterned road lines in municipal, private navigation, and OpenStreetMap datasets. The experimental testing shows that it has satisfactory results both in accuracy and completeness. F-measure is over 86% in hybrid-patterned Bosphorus datasets.
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一种利用相似度量的效率率来匹配道路线的新方法
不同数据集中对应的地理对象之间缺乏共同的语义信息,需要基于几何和拓扑度量的新匹配方法。在这项研究中,提出了一种基于几何和拓扑测度匹配能力的半自动匹配方法。在第一阶段,在由评分系统执行初始匹配之后,由操作员手动评估每个测量对匹配精度的效率。在第二阶段中,(1)根据精度分布来更新每个度量的得分。这意味着,如果一项指标比其他指标相对更重要,那么它的得分就会增加。最后,(2)用新的分数重复匹配过程。通过在市政、私人导航和OpenStreetMap数据集中匹配树型、蜂窝型和混合型道路线来测试所提出的方法。实验测试表明,该方法在准确性和完整性方面都取得了令人满意的结果。在混合模式博斯普鲁斯海峡数据集中,F-测度超过86%。
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CiteScore
4.00
自引率
0.00%
发文量
12
审稿时长
30 weeks
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