In recent years, Geo-information extraction from high-resolution satellite imagery has attracted a lot of attention. However, because of the high cost of image acquisition and annotation, there are limited datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc.). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. In this study, we propose GeoPalette, a Generative Adversarial Network (GAN) based tool to generate additional synthetic training samples for boosting model performance when the training dataset is limited. Our experiments on road segmentation show that using additional synthetic data can improves the model performance mean Intersection over Union (mIoU) from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model is standard-trained on 4,000 real pairs (64.59%), i.e., a 4-fold reduction in real dataset size.