  Rare object detection is a fundamental task in applied geospatial machine
learning, however is often challenging due to large amounts of high-resolution
satellite or aerial imagery and few or no labeled positive samples to start
with. This paper addresses the problem of bootstrapping such a rare object
detection task assuming there is no labeled data and no spatial prior over the
area of interest. We propose novel offline and online cluster-based approaches
for sampling patches that are significantly more efficient, in terms of
exposing positive samples to a human annotator, than random sampling. We apply
our methods for identifying bomas, or small enclosures for herd animals, in the
Serengeti Mara region of Kenya and Tanzania. We demonstrate a significant
enhancement in detection efficiency, achieving a positive sampling rate
increase from 2% (random) to 30%. This advancement enables effective machine
learning mapping even with minimal labeling budgets, exemplified by an F1 score
on the boma detection task of 0.51 with a budget of 300 total patches.
