  In this work, we propose a cross-view learning approach, in which images
captured from a ground-level view are used as weakly supervised annotations for
interpreting overhead imagery. The outcome is a convolutional neural network
for overhead imagery that is capable of predicting the type and count of
objects that are likely to be seen from a ground-level perspective. We
demonstrate our approach on a large dataset of geotagged ground-level and
overhead imagery and find that our network captures semantically meaningful
features, despite being trained without manual annotations.
