Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914702352711680 |
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| author | Zaytar, Akram Robinson, Caleb Hacheme, Gilles Q. Tadesse, Girmaw A. Dodhia, Rahul Ferres, Juan M. Lavista Hughey, Lacey F. Stabach, Jared A. Amoke, Irene |
| author_facet | Zaytar, Akram Robinson, Caleb Hacheme, Gilles Q. Tadesse, Girmaw A. Dodhia, Rahul Ferres, Juan M. Lavista Hughey, Lacey F. Stabach, Jared A. Amoke, Irene |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02736 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery Zaytar, Akram Robinson, Caleb Hacheme, Gilles Q. Tadesse, Girmaw A. Dodhia, Rahul Ferres, Juan M. Lavista Hughey, Lacey F. Stabach, Jared A. Amoke, Irene Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.02736 |