Identification and characterisation of urban building patterns using IKONOS imagery and point-based postal data

Abstract Point-based data recording the location of individual buildings are not only restoring enthusiasm in urban modelling but are also playing active roles in methodologies endeavouring to interpret urban land use from high spatial resolution remotely sensed images. One such approach explores the possibility of using both individual postal points to identify the spatial location of residential and commercial buildings as well as groups of postal points to characterise neighbourhood patterns from classified imagery. Postal points refer to those collected by the Ordnance Surveys of Great Britain (dataset known as ADDRESS-POINT™) and Northern Ireland (COMPAS™). Groups of these points are characterised using standard nearest-neighbour and linear nearest-neighbour indices in terms of the spacing and arrangement of residential and commercial buildings. The indices then form the basis for the interpretation of urban pixels classified from IKONOS imagery, along with a view to developing an automated pattern recognition system that would ultimately identify and characterise the physical arrangement of buildings in terms of density (compactness versus sparseness) and linearity. Encouraging results are documented from preliminary empirical testing on two cities in the United Kingdom using digital aerial photography at 25 cm spatial resolution.