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.

本文提出了一种解决罕见物体检测任务的方法，通过离线和在线聚类方法，显著提高样本曝光效率并实现有效的机器学习地图绘制。以肯尼亚和坦桑尼亚的塞伦盖蒂玛拉地区的 bomas 检测为例，实验证明了检测效率的显著提升，预算为 300 个样本总数时的 boma 检测任务的 F1 分数为 0.51。