Dear Colleagues, We're excited to announce our new open-access paper, in which we train a deep-learning algorithm to detect whales in high-resolution satellite imagery using images captured from aerial surveys. We hope that this can be a method that will help fill in some of our survey data gaps and help better target at-sea work such as tissue sampling and tag deployment. Aerial-trained deep learning networks for surveying cetaceans from satellite imagery https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212532 Borowicz A, Le H, Humphries G, Nehls G, Höschle C, Kosarev V, et al. (2019) Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS ONE 14(10): e0212532 Most cetacean species are wide-ranging and highly mobile, creating significant challenges for researchers by limiting the scope of data that can be collected and leaving large areas un-surveyed. Aerial surveys have proven an effective way to locate and study cetacean movements but are costly and limited in spatial extent. Here we present a semi-automated pipeline for whale detection from very high-resolution (sub-meter) satellite imagery that makes use of a convolutional neural network (CNN). We trained ResNet, and DenseNet CNNs using down-scaled aerial imagery and tested each model on 31 cm-resolution imagery obtained from the WorldView-3 sensor. Satellite imagery was tiled and the trained algorithms were used to classify whether or not a tile was likely to contain a whale. Our best model correctly classified 100% of tiles with whales, and 94% of tiles containing only water. All model architectures performed well, with learning rate controlling performance more than architecture. While the resolution of commercially-available satellite imagery continues to make whale identification a challenging problem, our approach provides the means to efficiently eliminate areas without whales and, in doing so, greatly accelerates ocean surveys for large cetaceans. Alex Borowicz PhD Candidate Ecology & Evolution Stony Brook University aborowicz.github.io 262-339-5413 "That's true enough," said Candide, "but we must go and work in the garden." Voltaire