Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
<|reference_start|>Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery: 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.<|reference_end|>
@article{zaytar2024bootstrapping,
    title={Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery},
    author={Akram Zaytar, Caleb Robinson, Gilles Q. Hacheme, Girmaw A. Tadesse,
  Rahul Dodhia, Juan M. Lavista Ferres, Lacey F. Hughey, Jared A. Stabach,
  Irene Amoke},
    journal={arXiv preprint arXiv:2403.02736},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02736},
    primaryClass={cs.CV cs.AI}
}