Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data

This repository contains the official code for the following Paper:

Hafner, S., Ban, Y. and Nascetti, A., 2022. Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data. Remote Sensing of Environment, 280, p.113192.

[Paper] [Dataset]

Generating built-up area maps

The quick and easy way:

All you need is a Google account with access to Google Earth Engine and Google Drive!

Then, follow these steps to generate built-up area maps for your own region of interest:

Create a folder named urban_extraction_app in your Google Drive

Download the model from here (Google Drive) and place it in the urban_extraction_app folder. Also make a copy of this Colab notebook in the same folder. Your folder should now contain the following files:

$ Your Google Drive setup
Your Google Drive
└── urban_extraction_app
    ├── urban_extraction_app.ipynb # this is the Colab notebook you can copied
    └── fusionda_10m.pt # this is the model you downloaded

Download satellite data for your region of interest with the UI in this GEE script.

Run the Colab notebook to generate a built-up area map for your region of interest.

Important: The model here only uses the 10 m bands of Sentinel-2 in comparison to the one in the paper which uses all 10 spectral bands. While the performance is similar, I will soon release a version which supports both models.

Or with the `inference.py` file in this repo:

1 Setup

Our setup uses Ubuntu 18.04.6 LTS, Python 3.9.7, PyTorch 1.10.0, and CUDA 11.4. Additionally, rasterio (1.2.10) is required to handle GeoTIFF files. To install the rasterio package on Windows, consider using the Unofficial Windows Binaries for Python Extension Packages.

2 Data download

The Sentinel-1 SAR and Sentinel-2 MSI data is downloaded from Google Earth Engine (GEE). Use the UI in this GEE script to select satellite data for your region of interest. Make sure to change 10m to all for the Sentinel-2 bands and check the tiling option!

After having run the script (and submitting the tasks in the Task panel), the Sentinel-1 and Sentinel-2 data will be in the Google Drive folders urban_extraction_sentinel1_*roi* and urban_extraction_sentinel2_*roi*, respectively.

Download the folders, rename them according to the sensor (i.e., sentinel1 and sentinel2), and place them in a folder named after your region of interest:

$ Satellite data directory
*data_dir*
└── *roi* # this folder can also be placed in your dataset directory as an additional site
    ├── sentinel1
    ├── sentinel1
    └── samples.json # this file will be added when running inference.py (step 3)

3 Download the pre-trained model

Download the pre-trained model and place it in the networks folder.

The pre-trained models from the paper can be downloaded from the following link: here (Google Drive). We strongly recommend using the proposed fusionda model or its light version fusionda_10m (only uses the 10 m Sentinel-2 bands).

Your networks folder should now contain the network file. Additionally, set up an inference folder:

$ Output data directory
output
├── networks
|    └── fusionda_checkpoint15.pt
└── inference

4 Run inference

Finally, run the inference.py file with the following arguments:

python inference.py -c fusionda -s *roi* -o *path to output directory* -d *path to data dir*

5 Stitching together the satellite patches (optional)

Training from scratch

If you want to train your own networks from scratch, follow these steps:

1 Dataset download

The SEN12 Global Urban Mapping (SEN12_GUM) dataset can be downloaded from Zenodo.

2 Network training

To train your network with our unsupervised domain adaptation approach, run the train_dualnetwork.py file with the fusionda.yaml config file:

python train_dualnetwork.py -c fusionda -o 'path to output directory' -d 'path to GM12_GUM dataset'

Likewise, the baselines can be replicated by running train_network.py with the configs sar.yaml, optical.yaml and fusion.yaml.

3 Model evaluation and inference

Run the files testing_quantitative.py and testing_qualitative.py with a config of choice and the path settings from above to assess network performance. For inference, use the file testing_inference.py instead.

4 Adding unlabeled data for further domain adaptation (optional)

Credits

If you find this work useful, please consider citing:

  @article{hafner2022unsupervised,
    title={Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data},
    author={Hafner, Sebastian and Ban, Yifang and Nascetti, Andrea},
    journal={Remote Sensing of Environment},
    volume={280},
    pages={113192},
    year={2022},
    publisher={Elsevier}
  }

SebastianHafner/DDA_UrbanExtraction