Spatial Data Integration for Classification of 3d Point Clouds from Digital Photogrammetry

Under increased urban settlement density, access to a high resolution (land-parcel scale) bare-earth Digital Elevation Model (DEM) is a pre-requisite for much decision support for planning: stormwater assessment, flood control, 3D visualisation, automatic delineation of flow paths, sub watersheds and flow networks for hydrological modelling. In these terms, a range of options face the DEM-building team. Apart from using necessarily expensive field survey, or use of out-of-date terrain information (usually in the form of digital contours of less-than-satisfactory interval) the model will be built from point-clouds. These will have been assembled via digital photogrammetry or acquisition of LiDAR data. In the first instance, both these data types soon yield a model that is known as a digital surface model (DSM). It includes any buildings, vehicles, vegetation (canopy and understory), as well as the “bare ground". To generate the required ’bare-earth' DEM, ground and non-ground features/data points must be distinguished from each other so that the latter can be eliminated before DEM building. Existing methods for doing this are based on data filtering routines, and are known to produce errors of omission and commission. Moreover, their implementation is complex and time consuming.