  Automatic building segmentation is an important task for satellite imagery
analysis and scene understanding. Most existing segmentation methods focus on
the case where the images are taken from directly overhead (i.e., low
off-nadir/viewing angle). These methods often fail to provide accurate results
on satellite images with larger off-nadir angles due to the higher noise level
and lower spatial resolution. In this paper, we propose a method that is able
to provide accurate building segmentation for satellite imagery captured from a
large range of off-nadir angles. Based on Bayesian deep learning, we explicitly
design our method to learn the data noise via aleatoric and epistemic
uncertainty modeling. Satellite image metadata (e.g., off-nadir angle and
ground sample distance) is also used in our model to further improve the
result. We show that with uncertainty modeling and metadata injection, our
method achieves better performance than the baseline method, especially for
noisy images taken from large off-nadir angles.
