  With deep learning becoming a more prominent approach for automatic
classification of three-dimensional point cloud data, a key bottleneck is the
amount of high quality training data, especially when compared to that
available for two-dimensional images. One potential solution is the use of
synthetic data for pre-training networks, however the ability for models to
generalise from synthetic data to real world data has been poorly studied for
point clouds. Despite this, a huge wealth of 3D virtual environments exist
which, if proved effective can be exploited. We therefore argue that research
in this domain would be of significant use. In this paper we present SynthCity
an open dataset to help aid research. SynthCity is a 367.9M point synthetic
full colour Mobile Laser Scanning point cloud. Every point is assigned a label
from one of nine categories. We generate our point cloud in a typical
Urban/Suburban environment using the Blensor plugin for Blender.
