Remote sensing and computer vision have the potential to enable comprehensive population monitoring to inform wildlife and biodiversity conservation. However, annotated datasets of wildlife in-situ are often difficult, expensive, and time-consuming to procure. This paper proposes a computational and data efficient method to synthesize composite images to supplement real-world data in data-sparse environments with few positive samples. We evaluated our method on three aerial remote sensing datasets and demonstrated a 3% increase in target-class IoU scores. We aim to use this method with a novel aerial dataset of the Boreal forest for ungulate monitoring, which is presently under development.