Characterisation of building alignments with new measures using C4.5 decision tree algorithm

Abstract

Detection and characterisation of spatial patterns is crucial for cartographic generalisation since it entails preserving the patterns as much as possible within scale limits. Building alignments are commonly confronted patterns in the topographic maps/databases. They are perceptually recognised in accordance with relevant Gestalt factors, namely proximity, similarity, common orientation and continuity. This study is concentrated on how to characterise building alignments detected by automated or manual methods. To this end, new measures based on Delaunay triangulation and regression line/curve are established to correspond to the Gestalt factors. The relationship between the measures and Gestalt principles has been illustrated with a decision tree. An index value was computed by total sum of measures’ values to compare and order alignments from quality aspect. Additionally, a supervised classification was performed with C4.5 algorithm thus a decision tree was obtained to be able to both associate the quality categories with the measure values and automatically assign alignments into a quality class. The findings demonstrate that proposed measures are substantially effective for representing Gestalt factors. The proposed methods can potentially enhance and ease the characterisation of building alignments in topographic map generalisation.