Generating Ground-Level Images From Overhead Imagery Using GANs with Yi Zhu

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Yi Zhu, a PhD candidate at UC Merced focused on geospatial image analysis.

In our conversation, Yi and I discuss his recent paper “What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks.” Yi and I discuss the goal of this research, which is to train effective land-use classifiers on proximate, or ground-level, images, and how he uses conditional GANs along with images sourced from social media to generate artificial ground-level images for this task. We also explore future research directions such as the use of reversible generative networks as proposed in the recently released OpenAI Glow paper to producing higher resolution images.

Sign up for our Newsletter!

Be sure to sign up for our weekly newsletter. We recently shared a write up detailing the ML/AI Job Board we’re working on, and got a ton of encouragement and interest. To make sure you don’t miss anything, head over to to sign up.

About Yi

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

Leave a Reply

Your email address will not be published.