  We present a novel multi-view training framework and CNN architecture for
combining information from multiple overlapping satellite images and noisy
training labels derived from OpenStreetMap (OSM) to semantically label
buildings and roads across large geographic regions (100 km$^2$). Our approach
to multi-view semantic segmentation yields a 4-7% improvement in the per-class
IoU scores compared to the traditional approaches that use the views
independently of one another. A unique (and, perhaps, surprising) property of
our system is that modifications that are added to the tail-end of the CNN for
learning from the multi-view data can be discarded at the time of inference
with a relatively small penalty in the overall performance. This implies that
the benefits of training using multiple views are absorbed by all the layers of
the network. Additionally, our approach only adds a small overhead in terms of
the GPU-memory consumption even when training with as many as 32 views per
scene. The system we present is end-to-end automated, which facilitates
comparing the classifiers trained directly on true orthophotos vis-a-vis first
training them on the off-nadir images and subsequently translating the
predicted labels to geographical coordinates. With no human supervision, our
IoU scores for the buildings and roads classes are 0.8 and 0.64 respectively
which are better than state-of-the-art approaches that use OSM labels and that
are not completely automated.
