Automatic Generation of Photorealistic Image Fillers for Privacy Enabled Urban Basemaps using Generative Adversarial Networks
Keywords: Geo Privacy, Deep Learning, GANs, Aerial Images, Buildings
Abstract. The abundance of high-quality satellite images is salutary for many activities but raises also privacy and security concerns. Manually obfuscating areas subject to privacy issues by applying locally pixelization techniques leads to undesirable discontinuities in the visual appearance of the depicted scenes. Alternatively, automatically generated photorealistic fillers can be used to obfuscate sensitive information while preserving the original visual aspect of high-resolution aerial images.
Recent advances in the field of Deep Learning (DL) enable to synthesize high-quality image data. Particularly, generative models such as Generative Adversarial Networks (GANs) can be used to produce images that can be perceived as photorealistic even by human examiners. Additionally, Conditional Generative Adversarial Networks (cGANs) allow control over the image generation process and results. These developments give the opportunity to generate photorealistic fillers for the purpose of privacy and security in image data used within city models while preserving the quality of the original data. However, according to our knowledge, little research has been done to explore this potential. In order to close this gap, we propose a novel framework that is designed to correspond to the mentioned end goal and produces promising results.