Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery

• URL: http://arxiv.org/abs/2403.02736v1
• Date: Tue, 5 Mar 2024 07:44:13 GMT
• Title: Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
• Authors: Akram Zaytar, Caleb Robinson, Gilles Q. Hacheme, Girmaw A. Tadesse, Rahul Dodhia, Juan M. Lavista Ferres, Lacey F. Hughey, Jared A. Stabach, Irene Amoke
• Abstract summary: This paper addresses the problem of bootstrapping such a rare object detection task. We propose novel offline and online cluster-based approaches for sampling patches. We apply our methods for identifying bomas, or small enclosures for herd animals, in the Serengeti Mara region of Kenya and Tanzania.
• Score: 2.242884292006914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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