We applied our approach on real-world examples and achieved an IoU between 0.930 and 0.998 assessed by ground truth footprints from both authoritative and volunteered geographic information (VGI) data. The segments are then generalized leading to the final footprint geometry. Outliers in the resulting segments are removed using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An ordering of the resulting points in this cut using a shortest possible tour based on TSP allows for the application of existing line segmentation algorithms, otherwise dedicated to indoor segmentation. Select the 'Enable automatic importing' check box to enable automatic importing of your PDFs. KDE turns out to be suitable to automatically determine a horizontal cut in the point cloud. To enable this option, follow the steps below: Open EndNote > click on Edit menu (in Windows) or the EndNote menu (in macOS) > select Preferences and then PDF Handling from the list of preferences. To this end, a pipeline of methods including non-parametric kernel density estimation (KDE) of an underlying probability density function, a solution of the Travelling Salesperson Problem (TSP), outlier elimination and line segmentation are presented to extract the underlying building footprint. This paper introduces an approach to extract a 2D building boundary from a 3D point cloud stemming from either terrestrial scanning or via close-range sensing using a mobile platform, e.g. Such structures are mostly derived using airborne laser scanning which reveals rather roof structures than the underlying hidden footprint boundary. Building footprints are a prerequisite for many tasks such as urban mapping and planning.
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