  The difficulty in obtaining labeled data relevant to a given task is among
the most common and well-known practical obstacles to applying deep learning
techniques to new or even slightly modified domains. The data volumes required
by the current generation of supervised learning algorithms typically far
exceed what a human needs to learn and complete a given task. We investigate
ways to expand a given labeled corpus of remote sensed imagery into a larger
corpus using Generative Adversarial Networks (GANs). We then measure how these
additional synthetic data affect supervised machine learning performance on an
object detection task.
  Our data driven strategy is to train GANs to (1) generate synthetic
segmentation masks and (2) generate plausible synthetic remote sensing imagery
corresponding to these segmentation masks. Run sequentially, these GANs allow
the generation of synthetic remote sensing imagery complete with segmentation
labels. We apply this strategy to the data set from ISPRS' 2D Semantic Labeling
Contest - Potsdam, with a follow on vehicle detection task. We find that in
scenarios with limited training data, augmenting the available data with such
synthetically generated data can improve detector performance.
