GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images

no code implementations • 14 Feb 2019 • Swetava Ganguli, Pedro Garzon, Noa Glaser

Our model translates the satellite image to the corresponding standard layer map image using three main model architectures: (i) a conditional Generative Adversarial Network (GAN) which compresses the images down to a learned embedding, (ii) a generator which is trained as a normalizing flow (RealNVP) model, and (iii) a conditional GAN where the generator translates via a series of convolutions to the standard layer of a map and the discriminator input is the concatenation of the real/generated map and the satellite image.

Generative Adversarial Network Style Transfer