Satellite Image Spoofing: Creating Remote Sensing Dataset with Generative Adversarial Networks (Short Paper)

Abstract

The rise of Artificial Intelligence (AI) has brought up both opportunities and challenges for today\u27s evolving GIScience. Its ability in image classification, object detection and feature extraction has been frequently praised. However, it may also apply for falsifying geospatial data. To demonstrate the thrilling power of AI, this research explored the potentials of deep learning algorithms in capturing geographic features and creating fake satellite images according to the learned \u27sense\u27. Specifically, Generative Adversarial Networks (GANs) is used to capture geographic features of a certain place from a group of web maps and satellite images, and transfer the features to another place. Corvallis is selected as the study area, and fake datasets with \u27learned\u27 style from three big cities (i.e. New York City, Seattle and Beijing) are generated through CycleGAN. The empirical results show that GANs can \u27remember\u27 a certain \u27sense of place\u27 and further apply that \u27sense\u27 to another place. With this paper, we would like to raise both public and GIScientists\u27 awareness in the potential occurrence of fake satellite images, and its impacts on various geospatial applications, such as environmental monitoring, urban planning, and land use development