From aerial LiDAR point clouds to multiscale urban representation levels by a parametric resampling

Urban simulations that involve disaster prevention, urban design, and assisted navigation heavily rely on urban geometric models. While large urban areas need a lot of time to be acquired terrestrially, government organizations have already conducted massive aerial LiDAR surveys, some even at the national level. This work aims to provide a pipeline for extracting multi-scale point clouds from 2D building footprints and airborne LiDAR data, which depends on whether the points represent buildings, vegetation, or ground. We denoise the roof slopes, match the vegetation, and roughly recreate the building fa & ccedil;ades frequently hidden to aerial acquisition using a parametric representation of geometric primitives. We then carry out multiple-scale samplings of the urban geometry until a 3D urban representation can be achieved because we annotate the new version of the original point cloud with the parametric equations representing each part. We mainly tested our methodology in a real-world setting - the city of Genoa - which includes historical buildings and is heavily characterized by irregular ground slopes. Moreover, we present the results of urban reconstruction on part of two other cities, Matera, which has a complex morphology like Genoa, and Rotterdam.