  Building segmentation is of great importance in the task of remote sensing
imagery interpretation. However, the existing semantic segmentation and
instance segmentation methods often lead to segmentation masks with blurred
boundaries. In this paper, we propose a novel instance segmentation network for
building segmentation in high-resolution remote sensing images. More
specifically, we consider segmenting an individual building as detecting
several keypoints. The detected keypoints are subsequently reformulated as a
closed polygon, which is the semantic boundary of the building. By doing so,
the sharp boundary of the building could be preserved. Experiments are
conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and
our method achieves better performance in both quantitative and qualitative
results with comparison to the state-of-the-art methods. Our network is a
bottom-up instance segmentation method that could well preserve geometric
details.
