@unpublished{zaytar2024bootstrapping,
author = {Zaytar, Akram and Robinson, Caleb and Hacheme, Gilles Quentin and Tadesse, Girmaw Abebe and Dodhia, Rahul and Lavista Ferres, Juan M. and Hughey, Lacey F. and Stabach, Jared A. and Amoke, Irene},
title = {Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery},
year = {2024},
month = {March},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/bootstrapping-rare-object-detection-in-high-resolution-satellite-imagery/},
}