Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
This work addresses the challenge of cross-view image synthesis for applications like remote sensing and urban planning, though it appears incremental as it builds on existing cGAN methods.
The paper tackles the problem of generating ground-level views from overhead imagery by developing a conditional generative adversarial network (cGAN) framework, which produces realistic ground-level images and can be adapted for land cover classification tasks.
This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network (cGAN) in which the trained generator generates realistic looking and representative ground-level images using overhead imagery as auxiliary information. The generator is an encoder-decoder network which allows us to compare low- and high-level features as well as their concatenation for encoding the overhead imagery. We also demonstrate how our framework can be used to perform land cover classification by modifying the trained cGAN to extract features from overhead imagery. This is interesting because, although we are using this modified cGAN as a feature extractor for overhead imagery, it incorporates knowledge of how locations look from the ground.