This is the official repo of GANmapper, a building footprint generator using Generative Adversarial Networks
Use environment.yml to create a conda environment for GANmapper
conda env create -f environment.yml
conda activate GANmapperThe weights files are available on figshare in the Checkpoints folder.
https://doi.org/10.6084/m9.figshare.15103128.v1
Place the Checkpoints folder in the repo.
Predictions can be carried out by running the following sample code. The name of the city depends on the name of each dataset.
python predict.py --dataroot <path to XYZ tile dir> --checkpoints_dir <path to checkpoint> --name <cityname> Testing an area in LA:
python predict.py --dataroot datasets/Exp4/LA/Source --checkpoints_dir checkpoints/Exp3 --name LA Testing an area in Singapore:
python predict.py --dataroot datasets/Exp4/Singapore/Source --checkpoints_dir checkpoints/Exp3 --name Singapore The result will be produced in XYZ directories in ./results/<cityname>/test_latest/images/fake
You can choose to visualise the tiles in QGIS using a local WMTS server.
For example, use the following url and choose Zomm 16 only.
file:///D:/GANmapper//results/Singapore/test_latest/images/fake/{z}/{x}/{y}.png
If you want the output to be in Geojson polygons, use extract.py
python extract.py <tile_dir> <out> python extract.py results/Exp4/LA/test_latest/images/fake LA.geojsonDistributed under the MIT License. See LICENSE for more information.
A paper about the work was published in International Journal of Geographical Information Science, and it is available open access here.
If you like this work and would like to use it in a scientific context, please cite this article.
Wu AN, Biljecki F (2022): GANmapper: geographical data translation. International Journal of Geographical Information Science, 36(7): 1394-1422. doi:10.1080/13658816.2022.2041643
@article{2022_ijgis_ganmapper,
author = {Wu, Abraham Noah and Biljecki, Filip},
doi = {10.1080/13658816.2022.2041643},
journal = {International Journal of Geographical Information Science},
title = {{GANmapper: geographical data translation}},
volume = {36},
issue = {7},
pages = {1394-1422},
year = {2022}
}
Abraham Noah Wu, Urban Analytics Lab, National University of Singapore, Singapore
This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start-Up Grant R-295-000-171-133.
We gratefully acknowledge the sources of the used input data.
GANmapper is made possible by using the following packages