Create a Location Generator GAN

This article explores the training of a FastCUT Generative Adversarial Network (GAN) model on map data and public e-bike feeds from cities across the USA. The trained GAN is then tested for its ability to learn and generalize by predicting location data sets for cities worldwide, including Tokyo. The process involves encoding e-bike location data as pixels into an image and training it as an image translation task similar to CycleGAN, Pix2pix, and StyleGAN. The newer contrastive unpaired translation (FastCUT) model is used due to its memory efficiency, fast training capabilities, and good generalization with minimal parameter tuning. The results show that the GAN can generate realistic location data for anywhere in the world, although there are some false positives, particularly in waterways.