GlobalMapper: Arbitrary-Shaped Urban Layout Generation

Official Pytorch Implementation of "GlobalMapper: Arbitrary-Shaped Urban Layout Generation"

arXiv | BibTeX | Project Page

This repo contains codes for single GPU training for GlobalMapper: Arbitrary-Shaped Urban Layout Generation

Note that this repo is lack of code comments.

Environment

We provide required environments in "environment.yml". But practially we suggest to use below commands for crucial dependencies:

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
pip install torch-geometric

Then you may install other dependencies like: matplotlib, yaml, pickle, etc.

Dataset

We provide a 10K dataset sample in the repo. You may directly unzip it ("dataset.tar.gz").

The 120K dataset is provided here

"processed" folder contains 10K preprocessed graph-represented city blocks. You may read them by "networkx.read_gpickle()". "raw_geo" contains 10K corresponding original building and block polygons (shapely.polygon format) of each city block (coordinates in UTM Zone projection). You may read it by "pickle.load()". Those original building polygons are directly acquired by "osmnx.geometries module" from osmnx.

Our canonical spatial transformation converts the original building polygons to the canonical version. After simple normalization by mean substraction and std dividing, coordinates and location information are encoded as node attributes in 2D grid graphs, then saved in "processed". Since the raw dataset is public accessible, we encourage users to implement their own preprocessing of original building polygons. It may facilitate better performance.

How to train your model

After set up your training parameters in "train_gnn.yaml". Simply run

python train.py

How to test your model

After you setup desired "dataset_path" and "epoch_name". Simply run

python test.py

How to do canonical spatial transformation

We provide a simple example in "example_canonical_transform.py". Details are provieded in our Supplemental Supp. We encourage users to commit their own realization.

How to do visualization

All maps in the paper are visualized by simple matplotlib draw functions that you may compose in minutes.

BibTeX

If you use this code, please cite

@InProceedings{He_2023_ICCV,
    author    = {He, Liu and Aliaga, Daniel},
    title     = {GlobalMapper: Arbitrary-Shaped Urban Layout Generation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {454-464}
}