Hierarchical Semantic Correspondence Analysis on Feature Classes between Two Geospatial Datasets Using a Graph Embedding Method

Abstract

A method to find corresponding feature class pairs, including hierarchical M:N pairs between two geospatial datasets is proposed. Applying an overlapping analysis to the object sets within the feature classes, the similarities of the feature classes are estimated and projected onto a lower-dimensional vector space after applying the graph embedding method. In this space, conventional mathematical tools—agglomerative hierarchical clustering in this study—could be used to analyze semantic correspondences between the datasets and identify their hierarchical M:N corresponding pairs. The proposed method was applied to two cadastral parcel datasets; one for latest land-use records in an urban information system, and the other, for original land-use categories in the Korea land information system. To quantitatively assess identified feature pairs, F-measures for each pair are presented. The results showed that it was possible to find various semantic correspondences of the feature classes and infer regional land development characteristics.