  The automated segmentation of buildings in remote sensing imagery is a
challenging task that requires the accurate delineation of multiple building
instances over typically large image areas. Manual methods are often laborious
and current deep-learning-based approaches fail to delineate all building
instances and do so with adequate accuracy. As a solution, we present Trainable
Deep Active Contours (TDACs), an automatic image segmentation framework that
intimately unites Convolutional Neural Networks (CNNs) and Active Contour
Models (ACMs). The Eulerian energy functional of the ACM component includes
per-pixel parameter maps that are predicted by the backbone CNN, which also
initializes the ACM. Importantly, both the ACM and CNN components are fully
implemented in TensorFlow and the entire TDAC architecture is end-to-end
automatically differentiable and backpropagation trainable without user
intervention. TDAC yields fast, accurate, and fully automatic simultaneous
delineation of arbitrarily many buildings in the image. We validate the model
on two publicly available aerial image datasets for building segmentation, and
our results demonstrate that TDAC establishes a new state-of-the-art
performance.
