Procedural 3D Terrain Generation using Generative Adversarial Networks (GANs)

A proposed method for generating random 3D lanscapes, imitating real data of satellite images and their respective Digital Elevation Models.

To generate random satellite images a GAN architecture called ProGAN was used following a great implementation of the paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation" (https://arxiv.org/abs/1710.10196) that can be found here.

For extracting a 3D mesh for the randomly generated satellite images, a CGAN architecture introduced in our previous work "Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning" ( paper, full code) was used.

Our python script that downloads an RGB satellite image corresponding to a DEM (or GeoJSON geometry) can be found here.

Samples of random outputs generated by the ProGAN

Random Images (ProGAN)	Predicted DEM (CGAN)	3D Visualization