Extracting urban features from LiDAR digital surface models

Abstract

The use of airborne Light Detection And Ranging (LiDAR) technology offers rapid high resolution capture of surface elevation data suitable for a large range of applications. The representation of both the ground surface and the features on that surface necessitates the removal of these surface features if a ground surface Digital Elevation Model (DEM) product is to be produced. This paper examines methods for extracting surface features from a Digital Surface Model (DSM) produced by LiDAR. It is argued that for some applications the extracted surface feature layer can be of almost equal importance to the DEM. The example of flood inundation modelling is used to illustrate how a DEM and a surface roughness layer could be extracted from the original DSM. The potential for refining surface roughness estimates by classifying extracted surface features using both topographic and spectral characteristics is considered using an Artificial Neural Network to discriminate between buildings and trees.