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Despite recent progress in computer vision, finegrained interpretation ofsatellite images remains challenging because of a lack of labeled trainingdata. To overcome this limitation, we construct a novel dataset calledWikiSatNet by pairing georeferenced Wikipedia articles with satellite imageryof their corresponding locations. We then propose two strategies to learnrepresentations of satellite images by predicting properties of thecorresponding articles from the images. Leveraging this new multi-modaldataset, we can drastically reduce the quantity of human-annotated labels andtime required for downstream tasks. On the recently released fMoW dataset, ourpre-training strategies can boost the performance of a model pre-trained onImageNet by up to 4:5% in F1 score.

Learning to Interpret Satellite Images in Global Scale Using Wikipedia