Spatial data integration for classification of 3D point clouds from digital photogrammetry

Abstract

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 Apart from using necessarily expensive field survey, or use of out-of-date terrain information (usually in the form of digital contours of less-than-satisfactory interval) the model will be built from point-clouds.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.I report here, the results of deploying spatial data integration instead of the previously favoured filtering routines. The challenge was to identify a process flow for separating the ground and non-ground points. It is shown that this alternative approach can be implemented if the client can supply a range of ancillary height data, this being most economically forthcoming if archivally available. The relative significance of these archival datasets emerges from exploring the various process flow paths and devising relevant quality tests designed to distinguish input suitable to support modelling at a land-parcel scale of analysis. Then the ArcGIS topological overlay technique was used to collect zonal statistics for each 3D point. Thus each output 3D point acquires z (elevation) values derived from the digital photogrammetry and z-statistics (minimum, maximum, mean) of its assigned zone. Clearly there is value in spatial data integration for a city with spatial data archives of adequately supportive scope and quality.