Chaudhry, Omair

Mackaness, William

2006-05-19T12:12:14Z

2006-05-19T12:12:14Z

2006

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)).

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Omair Chaudhry & William Mackaness (2005 Rural and Urban Road Network Generalisation Deriving 1:250,000 from OS MasterMap, online papers archived by the Institute of Geography, School of Geosciences, University of Edinburgh.

http://hdl.handle.net/1842/1137

en

Institute of Geography. The School of Geosciences.The University of Edinburgh

Institute of Geography Online Paper Series;GEO-015

Roads

Graph

Visual Perception

Generalisation

Strokes

Geography

Institute of Geography Online Papers Series (2005-2008)

Rural and urban road network generalisation: deriving 1:250,000 from OS MasterMap

Working Paper

Conference Paper