Mechanisms and processes for enhancing limited datasets for use in training or deep learning models are discussed. Such creation generates synthetic overhead imagery based on cluster sampling and spatial aggregator factors (SAFs). The implementation accomplishes this by generating Synthetic images created by cropping objects from original images and inserting them into uniform, natural or synthetic backgrounds. The objects are selected from clusters based on pixel distribution similarity, and through SAFs mining then used for the synthetic data generation.