  Antarctic penguins are important ecological indicators -- especially in the
face of climate change. In this work, we present a deep learning based model
for semantic segmentation of Ad\'elie penguin colonies in high-resolution
satellite imagery. To train our segmentation models, we take advantage of the
Penguin Colony Dataset: a unique dataset with 2044 georeferenced cropped images
from 193 Ad\'elie penguin colonies in Antarctica. In the face of a scarcity of
pixel-level annotation masks, we propose a weakly-supervised framework to
effectively learn a segmentation model from weak labels. We use a
classification network to filter out data unsuitable for the segmentation
network. This segmentation network is trained with a specific loss function,
based on the average activation, to effectively learn from the data with the
weakly-annotated labels. Our experiments show that adding weakly-annotated
training examples significantly improves segmentation performance, increasing
the mean Intersection-over-Union from 42.3 to 60.0% on the Penguin Colony
Dataset.
