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This paper investigates conditional generative adversarial networks (cGANs)to overcome a fundamental limitation of using geotagged media for geographicdiscovery, namely its sparse and uneven spatial distribution. We train a cGANto generate ground-level views of a location given overhead imagery. We showthe "fake" ground-level images are natural looking and are structurally similarto the real images. More significantly, we show the generated images arerepresentative of the locations and that the representations learned by thecGANs are informative. In particular, we show that dense feature maps generatedusing our framework are more effective for land-cover classification thanapproaches which spatially interpolate features extracted from sparseground-level images. To our knowledge, ours is the first work to use cGANs togenerate ground-level views given overhead imagery and to explore the benefitsof the learned representations.

What Is It Like Down There? Generating Dense Ground-Level Views and  Image Features From Overhead Imagery Using Conditional Generative Adversarial  Networks