Qualitative spatial reasoning under uncertainty in geographical information systems

The capability of geographical information systems (GIS) to display images and analyse them is one of the most powerful features, because people have a very consistent ability to reason about visual features according to their own knowledge model. Usually the abilities of GIS are restricted in dealing with quantitative data only, so that they fail whenever exact matches cannot be found. A solution to this problem is not only to allow the quantitative information, but also qualitative one. Proximity factors such as `close to´ and `far from´ are included. The qualitative approach offers a more natural way for the user of the system to specify what he actually wants, by using the proximity operators. This paper is an attempt of integrating qualitative spatial reasoning into geographic information systems. The idea is to associate qualitative information with fuzzy sets and to use the values of these fuzzy sets for spatial reasoning. The authors are concerned with the problem of image interpretation, which stands for the geometric and semantic recognition of the objects contained in one image