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

Abstract

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.

The Effects of Land Use and Land Cover Geoinformation Raster Generalization in the Analysis of LUCC in Portugal

“…The CLC vector dataset, due to its inherent characteristics/specifications, has greater generalization of LUC than the COS vector dataset. According to Yang et al [82], generalization of the LUC GI cannot dispense with the aggregation and amalgamation operations of the patch polygons. Comparing the COS and CLC outputs for different resolutions, the COS outputs are more spatially complex (see example in Figure 4), mainly due to the greater detail of the GI in this LUC dataset (minimum mapping unit (MMU) 1 ha).…”

“…The CLC vector dataset, due to its inherent characteristics/specifications, has greater generalization of LUC than the COS vector dataset. According to Yang et al [82], generalization of the LUC GI cannot dispense with the aggregation and amalgamation operations of the patch polygons.…”