UrbanControlNet

Introduction

The repository contains the code implementation of the paper: Envisioning Global Urban Development with Satellite Imagery and Generative AI.

Table of Contents

• Introduction
• Installation
• Dataset Preparation
• Model Training
• Model Testing
• Model evaluation
• Acknowledgements
• Citation
• License
• Contact Us

Installation

Download or clone the repository.

git clone https://github.com/kailaisun/UrbanControlNet.git
cd UrbanControlNet

We recommend using Conda (Miniconda) for installation.

Environment Installation

Create a virtual environment named mambacontrol and activate it.

conda env create -f environment.yaml
conda activate mambacontrol

Dataset Preparation

The dataset is built from publicly available global sources:

• Urban boundaries — GHS Urban Centre Database (2023), covering 500 metropolitan areas with 400 m × 400 m grids.
• Satellite imagery — Mapbox Static Tiles API.
• Population and building data — GHSL P2023A (2020): GHS-BUILT-S, GHS-BUILT-V, GHS-POP.
• Environmental constraints — OpenStreetMap, including major roads, water bodies, and railways.

Dataset Download

Download land use, building, and basemap data:

cd dataset
python download_mapbox_tiles.py  
python download_osm_landuse_building.py  

Create DEM, hint, and satellite images:

python create_dem_image.py  
python create_hint_image.py  
python create_satellite_image.py  

Compute grid density and land use–road metrics:

python compute_grid_density_gee.py  
python create_landuse_road_metrics.py  

Model Training

Finetuning

cd Urbancontrolnet
python train_density.py 

Finetuning decoder

Set sd_locked = False, then

python train_density.py

Model Testing

Checkpoints:

-We provide two checkpoints (one is finetuned on 100 cities with better performance; another is finetuned on 500 cities with larger coverage):Download

Urban Satelliate Image Generation:

Download checkpoints, make a new folder (checkpoints_density), and change the lines at results_view_loop_density.py:

ckpt_directory = f'./checkpoints_density'
ckpt_epoch_list = ['100'] 
output_file_dir = f'./output_image/city_'

python results_view_loop_density.py

Metric Prediction Model

Training

python prediction_train.py

Model evaluation

For computing metric (e.g., FID, ID, SSIM, FSIM, PSNR, etc.), please see our another repo: Evaluation-Metrics

Acknowledgements

Citation

License

The repository is licensed under the Apache 2.0 license.

Contact Us

If you have other questions❓, please contact us in time 👬