  Recently deep neural networks (DNNs) have achieved tremendous success for
object detection in overhead (e.g., satellite) imagery. One ongoing challenge
however is the acquisition of training data, due to high costs of obtaining
satellite imagery and annotating objects in it. In this work we present a
simple approach - termed Synthetic object IMPLantation (SIMPL) - to easily and
rapidly generate large quantities of synthetic overhead training data for
custom target objects. We demonstrate the effectiveness of using SIMPL
synthetic imagery for training DNNs in zero-shot scenarios where no real
imagery is available; and few-shot learning scenarios, where limited real-world
imagery is available. We also conduct experiments to study the sensitivity of
SIMPL's effectiveness to some key design parameters, providing users for
insights when designing synthetic imagery for custom objects. We release a
software implementation of our SIMPL approach so that others can build upon it,
or use it for their own custom problems.
