  Data imbalance is a ubiquitous problem in machine learning. In large scale
collected and annotated datasets, data imbalance is either mitigated manually
by undersampling frequent classes and oversampling rare classes, or planned for
with imputation and augmentation techniques. In both cases balancing data
requires labels. In other words, only annotated data can be balanced.
Collecting fully annotated datasets is challenging, especially for large scale
satellite systems such as the unlabeled NASA's 35 PB Earth Imagery dataset.
Although the NASA Earth Imagery dataset is unlabeled, there are implicit
properties of the data source that we can rely on to hypothesize about its
imbalance, such as distribution of land and water in the case of the Earth's
imagery. We present a new iterative method to balance unlabeled data. Our
method utilizes image embeddings as a proxy for image labels that can be used
to balance data, and ultimately when trained increases overall accuracy.
