  We propose to implicitly learn to extract geo-temporal image features, which
are mid-level features related to when and where an image was captured, by
explicitly optimizing for a set of location and time estimation tasks. To train
our method, we take advantage of a large image dataset, captured by outdoor
webcams and cell phones. The only form of supervision we provide are the known
capture time and location of each image. We find that our approach learns
features that are related to natural appearance changes in outdoor scenes.
Additionally, we demonstrate the application of these geo-temporal features to
time and location estimation.
