  In this paper we test the use of a deep learning approach to automatically
count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery. We
use a dataset of manually labelled imagery provided by the British Antarctic
Survey to train and develop our methods. We employ a U-Net architecture,
designed for image segmentation, to simultaneously classify and localise
potential albatrosses. We aid training with the use of the Focal Loss
criterion, to deal with extreme class imbalance in the dataset. Initial results
achieve peak precision and recall values of approximately 80%. Finally we
assess the model's performance in relation to inter-observer variation, by
comparing errors against an image labelled by multiple observers. We conclude
model accuracy falls within the range of human counters. We hope that the
methods will streamline the analysis of VHR satellite images, enabling more
frequent monitoring of a species which is of high conservation concern.
