DDA_UrbanExtraction

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DDA_UrbanExtraction

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

DDA_UrbanExtraction 최신버젼 다운로드

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# 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](https://doi.org/10.1016/j.rse.2022.113192)] [[Dataset](https://doi.org/10.5281/zenodo.6914898)]

# Generating built-up area maps

## The quick and easy way:

All you need is a Google account with access to [Google Earth Engine](https://earthengine.google.com/) and Google Drive!

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

1. Create a folder named `urban_extraction_app` in your Google Drive

2. Download the model from [here](https://drive.google.com/file/d/1CM_sr7v5PYDk52VDxh5xBDhH-ZWJQsgW/view?usp=sharing) (Google Drive) and place it in the `urban_extraction_app` folder. Also make a copy of this [Colab notebook](https://colab.research.google.com/drive/1JHDiYqMS7T7izH_ytPdmbVy5NgzP1YvB?usp=sharing) 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
    
    ```

3. Download satellite data for your region of interest with the UI in this [GEE script](https://code.earthengine.google.com/fd3790f3aa7b56e8be329effae4a575a?hideCode=true).

![](https://raw.githubusercontent.com/SebastianHafner/DDA_UrbanExtraction/master/figures/gee_app.gif)

4. 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](https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal).

### 2 Data download

The Sentinel-1 SAR and Sentinel-2 MSI data is downloaded from [Google Earth Engine](https://earthengine.google.com/) (GEE). Use the UI in this [GEE script](https://code.earthengine.google.com/fd3790f3aa7b56e8be329effae4a575a?hideCode=true) 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](https://drive.google.com/drive/folders/1GIVWbynLVZH_TyLb5eo6qgQq4Xdmm2Dg?usp=sharing) (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.

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6914898.svg)](https://doi.org/10.5281/zenodo.6914898)

## 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:

```bibtex
    @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}
    }
  ```

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