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Very large overhead imagery associated with ground truth maps has thepotential to generate billions of training image patches for machine learningalgorithms. However, random sampling selection criteria often leads toredundant and noisy-image patches for model training. With minimal researchefforts behind this challenge, the current status spells missed opportunitiesto develop supervised learning algorithms that generalize over widegeographical scenes. In addition, much of the computational cycles for largescale machine learning are poorly spent crunching through noisy and redundantimage patches. We demonstrate a potential framework to address these challengesspecifically, while evaluating a human settlement detection task. A novelbinary search tree sampling scheme is fused with a kernel based hashingprocedure that maps image patches into hash-buckets using binary codesgenerated from image content. The framework exploits inherent redundancy withinbillions of image patches to promote mostly high variance preserving samplesfor accelerating algorithmic training and increasing model generalization.

Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches