Environmental Mapping Based on Spatial Variability and Computational Vision Models
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Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Novel models of piecewise-homogeneous V images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Addition of spatial attributes v appears to be necessary in most areas where the differences in spatial data between regions in the image occur.