  While cloud/sky image segmentation has extensive real-world applications, a
large amount of labelled data is needed to train a highly accurate models to
perform the task. Scarcity of such volumes of cloud/sky images with
corresponding ground-truth binary maps makes it highly difficult to train such
complex image segmentation models. In this paper, we demonstrate the
effectiveness of using Generative Adversarial Networks (GANs) to generate data
to augment the training set in order to increase the prediction accuracy of
image segmentation model. We further present a way to estimate ground-truth
binary maps for the GAN-generated images to facilitate their effective use as
augmented images. Finally, we validate our work with different statistical
techniques.
