  Though semantic segmentation has been heavily explored in vision literature,
unique challenges remain in the remote sensing domain. One such challenge is
how to handle resolution mismatch between overhead imagery and ground-truth
label sources, due to differences in ground sample distance. To illustrate this
problem, we introduce a new dataset and use it to showcase weaknesses inherent
in existing strategies that naively upsample the target label to match the
image resolution. Instead, we present a method that is supervised using
low-resolution labels (without upsampling), but takes advantage of an exemplar
set of high-resolution labels to guide the learning process. Our method
incorporates region aggregation, adversarial learning, and self-supervised
pretraining to generate fine-grained predictions, without requiring
high-resolution annotations. Extensive experiments demonstrate the real-world
applicability of our approach.
