Proximate sensing: Inferring what-is-where from georeferenced photo collections

The primary and novel contribution of this work is the conjecture that large collections of georeferenced photo collections can be used to derive maps of what-is-where on the surface of the earth. We investigate the application of what we term “proximate sensing” to the problem of land cover classification for a large geographic region. We show that our approach is able to achieve almost 75% classification accuracy in a binary land cover labelling problem using images from a photo sharing site in a completely automated fashion. We also investigate 1) how existing geographic knowledge can be used to provide labelled training data in a weakly-supervised manner; 2) the effect of the photographer's intent when he or she captures the photograph; and 3) a method for filtering out non-informative images.