  Recently, the first foundation model developed specifically for image
segmentation tasks was developed, termed the "Segment Anything Model" (SAM).
SAM can segment objects in input imagery based on cheap input prompts, such as
one (or more) points, a bounding box, or a mask. The authors examined the
\textit{zero-shot} image segmentation accuracy of SAM on a large number of
vision benchmark tasks and found that SAM usually achieved recognition accuracy
similar to, or sometimes exceeding, vision models that had been trained on the
target tasks. The impressive generalization of SAM for segmentation has major
implications for vision researchers working on natural imagery. In this work,
we examine whether SAM's performance extends to overhead imagery problems and
help guide the community's response to its development. We examine SAM's
performance on a set of diverse and widely studied benchmark tasks. We find
that SAM does often generalize well to overhead imagery, although it fails in
some cases due to the unique characteristics of overhead imagery and its common
target objects. We report on these unique systematic failure cases for remote
sensing imagery that may comprise useful future research for the community.
