Sharing Visual Knowledge in Environmental Information Systems

Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Some competent models of piecewisehomogeneous images are used in environmental mapping to segment real images. These 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 appears to be necessary in most areas where the differences in spatial data between regions in the image occur. The problem under consideration is sharing visual knowledge about the probable environmental states of different types of land use or development of landscape.