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。

高分辨率遥感卫星图像中稀有目标检测的引导方法