  The world is covered with millions of buildings, and precisely knowing each
instance's position and extents is vital to a multitude of applications.
Recently, automated building footprint segmentation models have shown superior
detection accuracy thanks to the usage of Convolutional Neural Networks (CNN).
However, even the latest evolutions struggle to precisely delineating borders,
which often leads to geometric distortions and inadvertent fusion of adjacent
building instances. We propose to overcome this issue by exploiting the
distinct geometric properties of buildings. To this end, we present Deep
Structured Active Contours (DSAC), a novel framework that integrates priors and
constraints into the segmentation process, such as continuous boundaries,
smooth edges, and sharp corners. To do so, DSAC employs Active Contour Models
(ACM), a family of constraint- and prior-based polygonal models. We learn ACM
parameterizations per instance using a CNN, and show how to incorporate all
components in a structured output model, making DSAC trainable end-to-end. We
evaluate DSAC on three challenging building instance segmentation datasets,
where it compares favorably against state-of-the-art. Code will be made
available.
