Integration of Spatial Data Representing Buildings By Determining the Degree of Similarity

Spatial data integration is the process of combining heterogeneous data to make them compatible, maintain data accuracy and actuality, minimize redundancy, and avoid data conflicts. Identification of spatial objects is one of the most important tasks in spatial data integration. In this paper, we propose to use determination of the similarity measures for assessment of spatial object identification. The result is an implementation of the proposed procedure into the geographic information system. In the case study we update the ZBGIS® database by the data from the real estate cadastre system. The innovation of the presented method is that the proposed process and the new implemented software tools are based on determination of the similarity measures such as the Dice similarity index and the Jaccard similarity coefficient with its modification as the Tanimoto similarity coefficient.