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 |
|---|---|---|
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