In this paper we discuss uses of image segmentation, feature extraction and Bayesian networks for identifying buildings in digital orthophotos and the utilisation of the results for the automated computation of building statistics. Our work differs from previous attempts in a number of ways. Firstly, image segmentation is accomplished using an adaptive multi-scale method which brings together region and edge information to segment the image into regions. Secondly, automated building feature extraction (e.g. corners) is optimised to fit with expert human annotation performance. The third aspect of this work is the exploration of Bayesian networks as a method for fusing available information (ranging from corner information to solar angles as indicators of shadow location) to classify segmented regions as corresponding to buildings or not. Such processes then permit the automatic compilation of building statistics.
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Document Type: Research Article
Center for Mapping, The Ohio State University, Columbus, 43212, USA; Intercollege, 1700 Nicosia, Cyprus
Center for Mapping, The Ohio State University, Columbus, 43212, USA
Publication date: December 1, 2000
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