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Proceedings Paper

Study on spatial knowledge representation and reasoning based on Bayesian networks
Author(s): Jiejun Huang; Peipei Qi; Yanyan Wu; Yanbin Yuan; Fawang Ye
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Paper Abstract

Spatial information plays an essential role on the progress of science and technology, and has a profound impact on economic growth and society progress in the twenty-first century. Spatial knowledge representation and reasoning are very important for us to utilize spatial information. In this paper, a review is presented on spatial knowledge representation and reasoning. And then we propose a method of spatial knowledge representation and reasoning based on Bayesian networks. We focused on how to represent spatial relationship, spatial objects and spatial features by using Bayesian networks. Let spatial features (or spatial objects, spatial relationships) as variables or the nodes in Bayesian network, let directed edges present the relationships between spatial features, and the relevancy intensity can be regarded as confidence between the variables (the same as probability parameter in Bayesian network). Accordingly, the problem of spatial knowledge representation will be changed to the problem of learning Bayesian networks. The experimental results are given to verify the practical feasibility of the proposed methodology. Eventually, we conclude with a summary and a statement of future work.

Paper Details

Date Published: 11 November 2008
PDF: 9 pages
Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71461S (11 November 2008); doi: 10.1117/12.813156
Show Author Affiliations
Jiejun Huang, Wuhan Univ. of Technology (China)
Peipei Qi, Wuhan Univ. of Technology (China)
Yanyan Wu, Wuhan Univ. of Technology (China)
Yanbin Yuan, Wuhan Univ. of Technology (China)
Fawang Ye, National Key Lab. of Remote Sensing Information and Imagery Analysis (China)


Published in SPIE Proceedings Vol. 7146:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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