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Aerial imagery can be used for important work on a global scale.Nevertheless, the analysis of this data using neural network architectures lagsbehind the current state-of-the-art on popular datasets such as PASCAL VOC,CityScapes and Camvid. In this paper we bridge the performance-gap betweenthese popular datasets and aerial imagery data. Little work is done on aerialimagery with state-of-the-art neural network architectures in a multi-classsetting. Our experiments concerning data augmentation, normalisation, imagesize and loss functions give insight into a high performance setup for aerialimagery segmentation datasets. Our work, using the state-of-the-art DeepLabv3+Xception65 architecture, achieves a mean IOU of 70% on the DroneDeployvalidation set. With this result, we clearly outperform the current publiclyavailable 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 thebest performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52.5%.

Aerial Imagery Pixel-level Segmentation