35 - Representation of Positional Information

Abstract

Traditionally, space representation is a matter of computer vision, pattern recognition, and image analysis, which all use quantitative methods for dealing with space. In recent years, qualitative spatial reasoning—a subfield of artificial intelligence (AI)—has been developed with the aim of modeling commonsense knowledge of space with potential applications in areas as diverse as geographical information systems (GISs), computer aided design (CAD), and document recognition. In these areas, the concept of space is modified according to the scale from a tabletop environment to the largest geographic space, but it maintains its main characteristics. Qualitative models of spatial knowledge concern the description of both objects and their relative position in space. Qualitative models of positional information should be seen as models being able to represent commonsense knowledge of space and not as a surrogate of quantitative models, which have a specific role in computer graphics and pattern recognition. Qualitative models are especially suitable for describing positions in geographic space and building geographical information systems that simulate the mental processes of human beings. This chapter presents a conceptually unified theoretical framework within which positional relations can be represented and spatial reasoning can be performed to infer qualitative knowledge.