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Abstract and Applied Analysis
Volume 2014, Article ID 746094, 10 pages
Research Article

Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization

1Liaoning Normal University, Liaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, China
2Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received 4 April 2014; Accepted 14 May 2014; Published 2 June 2014

Academic Editor: Jianzhou Wang

Copyright © 2014 Jun Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the two evaluation elements (namely, semantic consistency and semantic completeness) from the semantic perspective to conduct the contrast evaluation study on the generalization results from the two levels, respectively, namely, land type and map. The study results show that: (1) before and after the generalization, it is easier for the vector data to guarantee the area balance of the patch; the raster data’s aggregation of the small patch is more obvious. (2) Analyzing from the scale of the land type, most of the land use types of the two kinds of generalization result’s semantic consistency is above 0.6; the semantic completeness of all types of land use in raster data is relatively low. (3) Analyzing from the scale of map, the semantic consistency of the generalization results for the two kinds of data is close to 1, while, in the aspect of semantic completeness, the land type deletion situation of the raster data generalization result is more serious.