Rural and Urban Road Network Generalisation Deriving 1:250,000 from OS MasterMap

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))