Abstract
Prediction of spatial attributes in geospatial data repositories is indispensable in the field of remote sensing and geographic information system. The semantic kriging ( SemK ) approach semantically captures the domain knowledge of the terrain in terms of local spatial features for spatial attribute prediction. It produces better results than ordinary kriging and other prediction methods. This letter focuses on the theoretical and empirical analyses of the SemK. A Euclidean vector analysis approach is adopted to theoretically prove the efficacy of SemK in capturing semantic knowledge.
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