Institute of Geography. The School of Geosciences.The University of Edinburgh
Abstract
Roads are essential component of topographic maps and spatial databases. The challenge in automated
generalisation of road networks is to derive a connected network while maintaining the structure for the intended target
scale and to achieve this with minimum user intervention. A lot of methods to select, displace and simplify roads have been
presented; the focus here is on the generalisation of networks using visual perception techniques. This paper presents a
framework based on visual perception that uses minimum attributes for generalisation of both ‘rural’ and ‘urban’ roads over
large scale change. The system incorporated graph theoretic techniques to explicitly model the topology of the network as it
was generalized. The model uses a fine scale map (1:1250 or 1:2500) as input and generates small scale (1:250,000) maps
directly from it without creating intermediate small scale maps. The results compared favorably with paper maps
(Ordnance Surveys Stratgie dataset (1:250,000))