Machine induction of geospatial knowledge

Abstract

Machine learning techniques such as tree induction have become accepted tools for developing generalisations of large data sets, typically for use with production rule systems in prediction and classification. The advent of computer based cartography and the field of geographic information systems (GIS) has seen a wealth of spatial data generated and used for decision making and modelling. We examine the implications of inductive techniques applied to geospatial data in a logical framework. It is argued that spatial induction systems will benefit from the ability to extend their initial representation language, through feature and relation construction. The enormous search spaces involved imply a need for strong biasing techniques to control the generation of possible representations of the data for all but the most trivial of cases. A heavily constrained geospatial domain, topographic representation, is described as one simplified example of induction across a vector description of space.