  Despite recent progress in computer vision, finegrained interpretation of
satellite images remains challenging because of a lack of labeled training
data. To overcome this limitation, we construct a novel dataset called
WikiSatNet by pairing georeferenced Wikipedia articles with satellite imagery
of their corresponding locations. We then propose two strategies to learn
representations of satellite images by predicting properties of the
corresponding articles from the images. Leveraging this new multi-modal
dataset, we can drastically reduce the quantity of human-annotated labels and
time required for downstream tasks. On the recently released fMoW dataset, our
pre-training strategies can boost the performance of a model pre-trained on
ImageNet by up to 4:5% in F1 score.
