  Recently deep learning - namely convolutional neural networks (CNNs) - have
yielded impressive performance for the task of building segmentation on large
overhead (e.g., satellite) imagery benchmarks. However, these benchmark
datasets only capture a small fraction of the variability present in real-world
overhead imagery, limiting the ability to properly train, or evaluate, models
for real-world application. Unfortunately, developing a dataset that captures
even a small fraction of real-world variability is typically infeasible due to
the cost of imagery, and manual pixel-wise labeling of the imagery. In this
work we develop an approach to rapidly and cheaply generate large and diverse
virtual environments from which we can capture synthetic overhead imagery for
training segmentation CNNs. Using this approach, generate and publicly-release
a collection of synthetic overhead imagery - termed Synthinel-1 with full
pixel-wise building labels. We use several benchmark dataset to demonstrate
that Synthinel-1 is consistently beneficial when used to augment real-world
training imagery, especially when CNNs are tested on novel geographic locations
or conditions.
