Airborne laser scanning (ALS) is becoming an increasingly popular data capture technique for a variety of applications in urban surface modeling. Raw ALS data are captured and supplied as a 3D point cloud. Many applications require that these data are interpolated onto a regular grid in order that they may be processed. In this article, we identify and analyze the magnitudes and spatial patterning of residuals from ALS models of urban surfaces, at a range of different scales. Previous research has demonstrated the effects of interpolation method and scale upon the nature of error in digital surface models (DSMs), but the size and spatial patterning of such errors have not hitherto been investigated for urban surfaces. The contribution of this analysis is thus to investigate the ways in which different methods may introduce error, and to understand the uncertainty that characterizes urban surface models that are devised for a wide range of applications. The importance of the research is shown using examples of how the different methods may introduce different amounts of error and how the uncertainty information may benefit users of ALS height models. Our analysis uses a range of validation techniques, including split-sample, cross-validation, and jackknifing, to estimate the error created in DSMs of urban areas. IntroductionDigital elevation models (DEMs) are used in many analytical operations such as slope and aspect calculations, and processes such as image segmentation and filtering in the creation of bare-earth digital terrain models (DTMs). When combined in applications, these operations can be highly sensitive to the quality of the elevation data used (Wood 1994;Florinsky 2002). However, many end users of height models in geographical information systems are unaware of the issues surrounding the quality of the underlying height data, partly because of the lack of diagnostic tools for dealing with quality information in many commercial software packages (Desmet 1997). Even where users are aware of quality issues with the DEM, there is a lack of methods available to accommodate them in subsequent spatial analysis (Goodchild and Gopal 1989). The sum consequence is a rather limited understanding of the uncertainties inherent in applications using DEMs based on airborne laser scanning (ALS) data.Previous research on error in DEMs has been restricted to analyzing the nature of the errors within bare-earth surfaces in predominantly natural environments (e.g., MacEachren and Davidson 1987;Xie et al. 2003).Very few studies have begun to consider the problem of understanding error in urban surface models. Assessment of the quality of urban digital surface models (DSMs) poses very different challenges to those of evaluating bare-earth models, because of the inherent complexities of the urban surface-including the frequent discontinuities, the variety of textures and shapes, and the rate of surface change. This paucity of error investigations for urban DEMs arises in spite of the fact that high spatial resolution, accu...