  An essential prerequisite for unleashing the potential of supervised deep
learning algorithms in the area of 3D scene understanding is the availability
of large-scale and richly annotated datasets. However, publicly available
datasets are either in relative small spatial scales or have limited semantic
annotations due to the expensive cost of data acquisition and data annotation,
which severely limits the development of fine-grained semantic understanding in
the context of 3D point clouds. In this paper, we present an urban-scale
photogrammetric point cloud dataset with nearly three billion richly annotated
points, which is three times the number of labeled points than the existing
largest photogrammetric point cloud dataset. Our dataset consists of large
areas from three UK cities, covering about 7.6 km^2 of the city landscape. In
the dataset, each 3D point is labeled as one of 13 semantic classes. We
extensively evaluate the performance of state-of-the-art algorithms on our
dataset and provide a comprehensive analysis of the results. In particular, we
identify several key challenges towards urban-scale point cloud understanding.
The dataset is available at https://github.com/QingyongHu/SensatUrban.
