Matching of urban pathways in a multi-scale database using fuzzy reasoning
One of the main steps of acquiring and handling data in a multi-scale database is generation of automatic links between corresponding objects in different scales, which is provided by matching them in the datasets. The basic concept of this process is to detect and measure the spatial similarity between various objects, which differ from one application to another, largely depends on the intrinsic properties of the input data. In fact, spatial similarity index, which is a function of other criteria such as geometric, topological, and semantic ones, is to some extent uncertain. Therefore, the present study aims to provide a matching algorithm based on fuzzy reasoning, while considering human spatial cognition. The proposed algorithm runs on two road datasets of Yazd city in Iran, which are in the scales of 1:5000 and 1:25000. The evaluation results show that matching rate and correctness of the algorithm is 92.7% and 88%, respectively, which validates the appropriate function of the proposed algorithm in matching.