Analysis of interpolation errors in urban digital surface models created from Lidar data

In urban areas Light Dectection and Ranging (LIDAR) data is becoming a widely available source for the construction of Digital Surface Models (DSMs). Because the urban surfaces have specific geometric characteristics such as discontinuities in terms of elevation and slope gradient, the interpolation of the irregularly spaced set of Lidar points to a regular grid have to be done carefully. In fact, many of the commercial GIS interpolation packages are based on the assumption that the surface is smoothly undulating. The choice of one of these interpolation functions will introduce errors across the surface models that they will be more expressive in the presence of surface discontinuities. Any subsequent analysis of the interpolated urban surface model, such as flood and viewshed analysis, feature extraction or image segmentation will be affected by propagation of these errors. In this paper, the pattern of errors and the general characteristics of six well-known interpolation methods (nearest neighbour, inverse distance weighting, triangulation with linear interpolation, minimum curvature, kriging and radial basis functions) are analysed using data either from synthetic surface models and from real urban scenes.