  In this paper we describe an approach to semi-automatically create a labelled
dataset for semantic segmentation of urban street-level point clouds. We use
data fusion techniques using public data sources such as elevation data and
large-scale topographical maps to automatically label parts of the point cloud,
after which only limited human effort is needed to check the results and make
amendments where needed. This drastically limits the time needed to create a
labelled dataset that is extensive enough to train deep semantic segmentation
models. We apply our method to point clouds of the Amsterdam region, and
successfully train a RandLA-Net semantic segmentation model on the labelled
dataset. These results demonstrate the potential of smart data fusion and
semantic segmentation for the future of smart city planning and management.
