Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
Akram Zaytar Caleb Robinson Gilles Q. Hacheme Girmaw A. Tadesse Rahul Dodhia Juan M. Lavista Ferres Lacey F. Hughey Jared A. Stabach Irene Amoke
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.