  Building footprint extraction in remote sensing data benefits many important
applications, such as urban planning and population estimation. Recently, rapid
development of Convolutional Neural Networks (CNNs) and open-sourced high
resolution satellite building image datasets have pushed the performance
boundary further for automated building extractions. However, CNN approaches
often generate imprecise building morphologies including noisy edges and round
corners. In this paper, we leverage the performance of CNNs, and propose a
module that uses prior knowledge of building corners to create angular and
concise building polygons from CNN segmentation outputs. We describe a new
transform, Relative Gradient Angle Transform (RGA Transform) that converts
object contours from time vs. space to time vs. angle. We propose a new shape
descriptor, Boundary Orientation Relation Set (BORS), to describe angle
relationship between edges in RGA domain, such as orthogonality and
parallelism. Finally, we develop an energy minimization framework that makes
use of the angle relationship in BORS to straighten edges and reconstruct sharp
corners, and the resulting corners create a polygon. Experimental results
demonstrate that our method refines CNN output from a rounded approximation to
a more clear-cut angular shape of the building footprint.
