  Aerial imagery can be used for important work on a global scale.
Nevertheless, the analysis of this data using neural network architectures lags
behind the current state-of-the-art on popular datasets such as PASCAL VOC,
CityScapes and Camvid. In this paper we bridge the performance-gap between
these popular datasets and aerial imagery data. Little work is done on aerial
imagery with state-of-the-art neural network architectures in a multi-class
setting. Our experiments concerning data augmentation, normalisation, image
size and loss functions give insight into a high performance setup for aerial
imagery segmentation datasets. Our work, using the state-of-the-art DeepLabv3+
Xception65 architecture, achieves a mean IOU of 70% on the DroneDeploy
validation set. With this result, we clearly outperform the current publicly
available state-of-the-art validation set mIOU (65%) performance with 5%.
Furthermore, to our knowledge, there is no mIOU benchmark for the test set.
Hence, we also propose a new benchmark on the DroneDeploy test set using the
best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52.5%.
