Three dimensional representations of surface terrain and structure is essential for a range of widespread applications and forms a base dataset that underlies many decision making processes. A few examples include land use planning, areal overview, operational analysis, emergency handling, route and transport planning, geographical and meteorological modelling etc. Recently, the Norwegian Government and the Norwegian Mapping Authority tasked the acquisition of high resolution Light Detection and Ranging (LIDAR) data covering the entire mainland with a minimum of 2 point measurements per meter. In addition, all aerial lidar acquisitions that were tasked by the government since the early 2000s are also publically available for download. In this work using FOSS, we discuss the height accuracy of ground classified datasets (i.e. Digital Terrain Models, Digital Surface Models) with varying original acquisition ground point densities. We create classification pipelines that allow us to calculate derivative products such as a “normalized” vegetation density and further compare these over time. This work in progress discusses our experience with open source tools on open source data and some of the challenges we encountered scaling our methods for big data.