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.