  Very large overhead imagery associated with ground truth maps has the
potential to generate billions of training image patches for machine learning
algorithms. However, random sampling selection criteria often leads to
redundant and noisy-image patches for model training. With minimal research
efforts behind this challenge, the current status spells missed opportunities
to develop supervised learning algorithms that generalize over wide
geographical scenes. In addition, much of the computational cycles for large
scale machine learning are poorly spent crunching through noisy and redundant
image patches. We demonstrate a potential framework to address these challenges
specifically, while evaluating a human settlement detection task. A novel
binary search tree sampling scheme is fused with a kernel based hashing
procedure that maps image patches into hash-buckets using binary codes
generated from image content. The framework exploits inherent redundancy within
billions of image patches to promote mostly high variance preserving samples
for accelerating algorithmic training and increasing model generalization.

Computer Vision and Pattern Recognition