  Most state-of-the-art approaches to road extraction from aerial images rely
on a CNN trained to label road pixels as foreground and remainder of the image
as background. The CNN is usually trained by minimizing pixel-wise losses,
which is less than ideal to produce binary masks that preserve the road
network's global connectivity. To address this issue, we introduce an
Adversarial Learning (AL) strategy tailored for our purposes. A naive one would
treat the segmentation network as a generator and would feed its output along
with ground-truth segmentations to a discriminator. It would then train the
generator and discriminator jointly. We will show that this is not enough
because it does not capture the fact that most errors are local and need to be
treated as such. Instead, we use a more sophisticated discriminator that
returns a label pyramid describing what portions of the road network are
correct at several different scales. This discriminator and the structured
labels it returns are what gives our approach its edge and we will show that it
outperforms state-of-the-art ones on the challenging RoadTracer dataset.

Computer Vision and Pattern Recognition