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In this paper, we propose a Deep Active Ray Network (DARNet) for automaticbuilding segmentation. Taking an image as input, it first exploits a deepconvolutional neural network (CNN) as the backbone to predict energy maps,which are further utilized to construct an energy function. A polygon-basedcontour is then evolved via minimizing the energy function, of which theminimum defines the final segmentation. Instead of parameterizing the contourusing Euclidean coordinates, we adopt polar coordinates, i.e., rays, which notonly prevents self-intersection but also simplifies the design of the energyfunction. Moreover, we propose a loss function that directly encourages thecontours to match building boundaries. Our DARNet is trained end-to-end byback-propagating through the energy minimization and the backbone CNN, whichmakes the CNN adapt to the dynamics of the contour evolution. Experiments onthree building instance segmentation datasets demonstrate our DARNet achieveseither state-of-the-art or comparable performances to other competitors.

DARNet: Deep Active Ray Network for Building Segmentation