README.md

---
license: other
license_name: open-licence-2.0
license_link: https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf
pretty_name: French Land Cover from Aerospace Imagery
size_categories:
- 10B<n<100B
---



# Challenge FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery

Code : [![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/IGNF/FLAIR-1-AI-Challenge/blob/master/LICENSE) &emsp; Dataset : [![license](https://img.shields.io/badge/License-IO%202.0-green.svg)](https://github.com/etalab/licence-ouverte/blob/master/open-licence.md)

Participate in obtaining more accurate maps for a more comprehensive description and a better understanding of our environment! Come push the limits of state-of-the-art semantic segmentation approaches on a large and challenging dataset. Get in touch at ai-challenge@ign.fr


![Alt bandeau FLAIR-IGN](images/flair_bandeau.jpg?raw=true)
<img width="100%" src="images/flair-2_logos-hd.png">

<div style="border-width:1px; border-style:solid; border-color:#d2db8c; padding-left: 1em; padding-right: 1em; ">
  
<h2 style="margin-top:5px;">Links</h2>


- **Datapaper : https://arxiv.org/pdf/2305.14467.pdf** 

- **Dataset links :** https://ignf.github.io/FLAIR/#FLAIR2

- **Challenge page : https://codalab.lisn.upsaclay.fr/competitions/13447** 

</div>
<br><br>





## Context & Data

The FLAIR #2 dataset is sampled countrywide and is composed of over 20 billion annotated pixels of very high resolution aerial imagery at 0.2 m spatial resolution, acquired over three years and different months (spatio-temporal domains). Aerial imagery patches consist of 5 channels (RVB-Near Infrared-Elevation) and have corresponding annotation (with 19 semantic classes or 13 for the baselines). Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 1-year time series with 10 spectral band are also provided. More than 50,000 Sentinel-2 acquisitions with 10 m spatial resolution are available.
<br>

The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km². With 13 semantic classes (plus 6 not used in this challenge), this dataset provides a robust foundation for advancing land cover mapping techniques.<br><br>

<center>
<table style="width:80%;max-width:700px;height:200px">
<thead>
  <tr><th width=7% height=></th><th>Class</th><th style='text-align: center' width=15%>Value</th><th style='text-align: center'>Freq.-train (%)</th><th style='text-align: center'>Freq.-test (%)</th></tr>
</thead>
<tbody>
  <tr><td bgcolor='#db0e9a'></td><td>building</td><td style='text-align: center'>1</td><td style='text-align: center'>8.14</td><td style='text-align: center'>3.26</td></tr>
  
  <tr><td bgcolor='#938e7b'></td><td>pervious surface</td><td style='text-align: center'>2</td><td style='text-align: center'>8.25</td><td style='text-align: center'>3.82</td></tr>
  
  <tr><td bgcolor='#f80c00'></td><td>impervious surface</td><td style='text-align: center'>3</td><td style='text-align: center'>13.72</td><td style='text-align: center'>5.87</td></tr>
  
  <tr><td bgcolor='#a97101'></td><td>bare soil</td><td style='text-align: center'>4</td><td style='text-align: center'>3.47</td><td style='text-align: center'>1.6</td></tr>
  
  <tr><td bgcolor='#1553ae'></td><td>water</td><td style='text-align: center'>5</td><td style='text-align: center'>4.88</td><td style='text-align: center'>3.17</td></tr>
  
  <tr><td bgcolor='#194a26'></td><td>coniferous</td><td style='text-align: center'>6</td><td style='text-align: center'>2.74</td><td style='text-align: center'>10.24</td></tr>
  
  <tr><td bgcolor='#46e483'></td><td>deciduous</td><td style='text-align: center'>7</td><td style='text-align: center'>15.38</td><td style='text-align: center'>24.79</td></tr>
  
  <tr><td bgcolor='#f3a60d'></td><td>brushwood</td><td style='text-align: center'>8</td><td style='text-align: center'>6.95</td><td style='text-align: center'>3.81</td></tr>
  
  <tr><td bgcolor='#660082'></td><td>vineyard</td><td style='text-align: center'>9</td><td style='text-align: center'>3.13</td><td style='text-align: center'>2.55</td></tr>
  
  <tr><td bgcolor='#55ff00'></td><td>herbaceous vegetation</td><td style='text-align: center'>10</td><td style='text-align: center'>17.84</td><td style='text-align: center'>19.76</td></tr>
  
  <tr><td bgcolor='#fff30d'></td><td>agricultural land</td><td style='text-align: center'>11</td><td style='text-align: center'>10.98</td><td style='text-align: center'>18.19</td></tr>
  
  <tr><td bgcolor='#e4df7c'></td><td>plowed land</td><td style='text-align: center'>12</td><td style='text-align: center'>3.88</td><td style='text-align: center'>1.81</td></tr>
  
  <tr><td bgcolor='#3de6eb'></td><td>swimming pool</td><td style='text-align: center'>13</td><td style='text-align: center'>0.01</td><td style='text-align: center'>0.02</td></tr>
  
  <tr><td bgcolor='#ffffff'></td><td>snow</td><td style='text-align: center'>14</td><td style='text-align: center'>0.15</td><td style='text-align: center'>-</td></tr>
  
  <tr><td bgcolor='#8ab3a0'></td><td>clear cut</td><td style='text-align: center'>15</td><td style='text-align: center'>0.15</td><td style='text-align: center'>0.82</td></tr>
  
  <tr><td bgcolor='#6b714f'></td><td>mixed</td><td style='text-align: center'>16</td><td style='text-align: center'>0.05</td><td style='text-align: center'>0.12</td></tr>
  
  <tr><td bgcolor='#c5dc42'></td><td>ligneous</td><td style='text-align: center'>17</td><td style='text-align: center'>0.01</td><td style='text-align: center'>-</td></tr>
  
  <tr><td bgcolor='#9999ff'></td><td>greenhouse</td><td style='text-align: center'>18</td><td style='text-align: center'>0.12</td><td style='text-align: center'>0.15</td></tr>
  
  <tr><td bgcolor='#000000'></td><td>other</td><td style='text-align: center'>19</td><td style='text-align: center'>0.14</td><td style='text-align: center'>0.04</td></tr>
</tbody>
</table>
</center>


<br><br>


## Usage 

The `flair-2-config.yml` file controls paths, hyperparameters and computing ressources. The file `requirement.txt` is listing used libraries for the baselines.

To launch a training/inference/metrics computation, you can either use : 

- ```
  main.py --config_file=flair-2-config.yml
  ```

-  use the `./notebook/flair-2-notebook.ipynb` notebook guiding you through data visualization, training and testing steps.

A toy dataset (reduced size) is available to check that your installation and the information in the configuration file are correct.

<br><br>


## Reference
Please include a citation to the following article if you use the FLAIR #2 dataset:

```
@article{ign2023flair2,
  doi = {10.13140/RG.2.2.30938.93128/2},
  url = {https://arxiv.org/pdf/2305.14467.pdf},
  author = {Garioud, Anatol and {DE Wit}, Apolline and Poupée, Marc and Valette, Marion and Giordano, Sébastien and Wattrelos, Boris},
  title = {FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery},
  publisher = {arXiv},
  year = {2023}
}
```

## Acknowledgment
This work was performed using HPC/AI resources from GENCI-IDRIS (Grant 2022-A0131013803). This work was supported by the project "Copernicus / FPCUP” of the European Union, by the French Space Agency (CNES) and by Connect by CNES.<br>





## Dataset license

The "OPEN LICENCE 2.0/LICENCE OUVERTE" is a license created by the French government specifically for the purpose of facilitating the dissemination of open data by public administration. 
If you are looking for an English version of this license, you can find it on the official GitHub page at the [official github page](https://github.com/etalab/licence-ouverte).

As stated by the license :

### Applicable legislation

This licence is governed by French law.

### Compatibility of this licence

This licence has been designed to be compatible with any free licence that at least requires an acknowledgement of authorship, and specifically with the previous version of this licence as well as with the following licences: United Kingdom’s “Open Government Licence” (OGL), Creative Commons’ “Creative Commons Attribution” (CC-BY) and Open Knowledge Foundation’s “Open Data Commons Attribution” (ODC-BY).