SpaceGAN - A generative adverserial net for geospatial point data

This repository provides complementary code and data for the paper "Augmenting Correlation Structures in Spatial Data Using Deep Generative Models" (arXiv:1905.09796).

SpaceGAN applies a conditional GAN (CGAN) with neighbourhood conditioning to learn local spatial autocorrelation structures.

Structure

The src folder contains the raw SpaceGAN codebase and utility functions. The folder data contains the datasets used in the experiments.

Interactive version

However we recommend to try out SpaceGAN using the interactive notebooks provided in the main folder. These support Google Colab and can be run here:

(1) SpaceGAN with geospatial data

• Experiment_01_Toy1
• Experiment_02_Toy2
• Experiment_03_CaliHousing

(2) MIE selection

• Experiment_04_MIE_CGAN_MNIST

Citation

@article{klemmer2019spacegan,
  title={Augmenting correlation structures in spatial data using deep generative models},
  author={Klemmer, Konstantin and Koshiyama, Adriano and Flennerhag, Sebastian},
  journal={arXiv preprint arXiv:1905.09796},
  year={2019}
}