| Summary: | In recent times the demand of high resolution 3D data has seen a rise, and the applications of airborne LiDAR data are increasing. Automatic extraction of building roofs is important in many of these applications such as city modelling. In 2018, Lantmäteriet (the Swedish mapping, cadastral and land registration authority) is planning a new flight to collect airborne LiDAR data. This data may become useful in extracting roof planes. The purpose of this thesis is to evaluate automatic methods for extracting buildings from airborne LiDAR data, and to perform a quality assessment of the footprints.This thesis proposes specific methods of extraction in using software called ArcGIS Pro and FME. The method was to process raw LiDAR points by separating the ground points, and finding building points through plane detection of points in clusters. Vegetation was removed using height difference of the points and the area. Polygons were created from the building points and a quality assessment was then carried out concerning completeness, accuracy and RMSE. The result on four different data sets shows a more appropriate extraction in FME. Lower point density sometimes leads to better extraction of buildings because of less vegetation. Higher point density has the advantage of higher accuracy and can extract smaller buildings, but includes more vegetation. The proposed method is recommended for larger buildings (>25 m2) and a LiDAR point density around 12 points/m2.
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