  Semantic segmentation of aerial point cloud data can be utilised to
differentiate which points belong to classes such as ground, buildings, or
vegetation. Point clouds generated from aerial sensors mounted to drones or
planes can utilise LIDAR sensors or cameras along with photogrammetry. Each
method of data collection contains unique characteristics which can be learnt
independently with state-of-the-art point cloud segmentation models. Utilising
a single point cloud segmentation model can be desirable in situations where
point cloud sensors, quality, and structures can change. In these situations it
is desirable that the segmentation model can handle these variations with
predictable and consistent results. Although deep learning can segment point
clouds accurately it often suffers in generalisation, adapting poorly to data
which is different than the training data. To address this issue, we propose to
utilise multiple available open source fully annotated datasets to train and
test models that are better able to generalise.
  In this paper we discuss the combination of these datasets into a simple
training set and challenging test set. Combining datasets allows us to evaluate
generalisation performance on known variations in the point cloud data. We show
that a naive combination of datasets produces a model with improved
generalisation performance as expected. We go on to show that an improved
sampling strategy which decreases sampling variations increases the
generalisation performance substantially on top of this. Experiments to find
which sample variations give this performance boost found that consistent
densities are the most important.
