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arXiv · 1809.10236

Learning to Interpret Satellite Images Using Wikipedia

Evan Sheehan·Burak Uzkent·Chenlin Meng·Zhongyi Tang·Marshall Burke·David Lobell·Stefano Ermon
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Abstract

Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we propose using Wikipedia as a previously untapped source of rich, georeferenced textual information with global coverage. We construct a novel large-scale, multi-modal dataset by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations. To prove the efficacy of this dataset, we focus on the African continent and train a deep network to classify images based on labels extracted from articles. We then fine-tune the model on a human annotated dataset and demonstrate that this weak form of supervision can drastically reduce the quantity of human annotated labels and time required for downstream tasks.

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Evan Sheehan, Burak Uzkent, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon. 2018-09-19. Learning to Interpret Satellite Images Using Wikipedia. https://arxiv.org/abs/1809.10236

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