  Semantic segmentation of satellite imagery is a common approach to identify
patterns and detect changes around the planet. Most of the state-of-the-art
semantic segmentation models are trained in a fully supervised way using
Convolutional Neural Network (CNN). The generalization property of CNN is poor
for satellite imagery because the data can be very diverse in terms of
landscape types, image resolutions, and scarcity of labels for different
geographies and seasons. Hence, the performance of CNN doesn't translate well
to images from unseen regions or seasons. Inspired by Conditional Generative
Adversarial Networks (CGAN) based approach of image-to-image translation for
high-resolution satellite imagery, we propose a CGAN framework for land cover
classification using medium-resolution Sentinel-2 imagery. We find that the
CGAN model outperforms the CNN model of similar complexity by a significant
margin on an unseen imbalanced test dataset.
