A visual geographic knowledge classification and its relationship to the KADS model extended summary

Abstract

Visual geographic knowledge which can be extracted from satellite remote sensing images has characteristics which are not commonly found in non-visual domains. Traditionally geographic expert systems have worked either at the pixel level of raster images or the object level of vector images. This has shortfalls when knowledge acquisition from a human image interpreter has to be incorporated into an expert system to aid interpretation. A framework for the classification of visual geographic knowledge will be presented that expands beyond the traditional per-pixel model and has been used as the theoretical basis of a knowledge acquisition toolkit, KAGES (Knowledge Acquisition for Geographic Expert Systems) [2]. This model will be compared with the KADS knowledge model to show the relationship with modeling in a non-visual environment.