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README.md

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-# 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>
-
-
+# Datset Card for FLAIR land-cover semantic segmentation
 
 
 
 ## 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.
+The hereby 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">
+The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km². This dataset provides a robust foundation for advancing land cover mapping techniques.<br><br>
+<style type="text/css">
+.tg  {border-collapse:collapse;border-spacing:0;}
+.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
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+.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
+  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
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+.tg .tg-jmwx{background-color:#f3a60d;border-color:#ffffff;text-align:left;vertical-align:top}
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+.tg .tg-x5zi{background-color:#8ab3a0;border-color:#ffffff;text-align:left;vertical-align:top}
+</style>
+<table class="tg">
 <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>
+  <tr>
+    <th class="tg-zv4m"></th>
+    <th class="tg-zv4m">Class</th>
+    <th class="tg-8jgo">Freq.-train(%)</th>
+    <th class="tg-8jgo">Freq.-test(%)</th>
+    <th class="tg-zv4m"></th>
+    <th class="tg-zv4m">Class</th>
+    <th class="tg-8jgo">Freq.-train(%)</th>
+    <th class="tg-8jgo">Freq.-test(%)</th>
+    <th class="tg-zv4m"></th>
+    <th class="tg-zv4m">Class</th>
+    <th class="tg-8jgo">Freq.-train(%)</th>
+    <th class="tg-8jgo">Freq.-test(%)</th>
+    <th class="tg-zv4m"></th>
+    <th class="tg-zv4m">Class</th>
+    <th class="tg-8jgo">Freq.-train(%)</th>
+    <th class="tg-8jgo">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>
+  <tr>
+    <td class="tg-2e1p"></td>
+    <td class="tg-km2t">(1) Building</td>
+    <td class="tg-8jgo">8.14</td>
+    <td class="tg-8jgo">3.26</td>
+    <td class="tg-j3z6"></td>
+    <td class="tg-km2t">(6) Coniferous</td>
+    <td class="tg-8jgo">2.74</td>
+    <td class="tg-8jgo">10.24</td>
+    <td class="tg-pop6"></td>
+    <td class="tg-km2t">(11) Agricultural Land</td>
+    <td class="tg-8jgo">10.98</td>
+    <td class="tg-8jgo">18.19</td>
+    <td class="tg-7f0h"></td>
+    <td class="tg-km2t">(16) Mixed</td>
+    <td class="tg-8jgo">0.05</td>
+    <td class="tg-8jgo">0.12</td>
+  </tr>
+  <tr>
+    <td class="tg-9efv"></td>
+    <td class="tg-km2t">(2) Pervious surface</td>
+    <td class="tg-8jgo">8.25</td>
+    <td class="tg-8jgo">3.82</td>
+    <td class="tg-edjf"></td>
+    <td class="tg-km2t">(7) Deciduous</td>
+    <td class="tg-8jgo">15.38</td>
+    <td class="tg-8jgo">24.79</td>
+    <td class="tg-3chm"></td>
+    <td class="tg-km2t">(12) Plowed land</td>
+    <td class="tg-8jgo">3.88</td>
+    <td class="tg-8jgo">1.81</td>
+    <td class="tg-40e0"></td>
+    <td class="tg-km2t">(17) Ligneous</td>
+    <td class="tg-8jgo">0.01</td>
+    <td class="tg-8jgo">-</td>
+  </tr>
+  <tr>
+    <td class="tg-3m6m"></td>
+    <td class="tg-km2t">(3) Impervious surface</td>
+    <td class="tg-8jgo">13.72</td>
+    <td class="tg-8jgo">5.87</td>
+    <td class="tg-jmwx"></td>
+    <td class="tg-km2t">(8) Brushwood</td>
+    <td class="tg-8jgo">6.95</td>
+    <td class="tg-8jgo">3.81</td>
+    <td class="tg-kors"></td>
+    <td class="tg-km2t">(13) Swimming pool</td>
+    <td class="tg-8jgo">0.01</td>
+    <td class="tg-8jgo">0.02</td>
+    <td class="tg-qwc7"></td>
+    <td class="tg-km2t">(18) Greenhouse</td>
+    <td class="tg-8jgo">0.12</td>
+    <td class="tg-8jgo">0.15</td>
+  </tr>
+  <tr>
+    <td class="tg-r3rw"></td>
+    <td class="tg-km2t">(4) Bare soil</td>
+    <td class="tg-8jgo">3.47</td>
+    <td class="tg-8jgo">1.6</td>
+    <td class="tg-69kt"></td>
+    <td class="tg-km2t">(9) Vineyard</td>
+    <td class="tg-8jgo">3.13</td>
+    <td class="tg-8jgo">2.55</td>
+    <td class="tg-oe15"></td>
+    <td class="tg-km2t">(14) Snow</td>
+    <td class="tg-8jgo">0.15</td>
+    <td class="tg-8jgo">-</td>
+    <td class="tg-oedl"></td>
+    <td class="tg-km2t">(19) Other</td>
+    <td class="tg-8jgo">0.14</td>
+    <td class="tg-8jgo">0.04</td>
+  </tr>
+  <tr>
+    <td class="tg-9xgv"></td>
+    <td class="tg-km2t">(5) Water</td>
+    <td class="tg-8jgo">4.88</td>
+    <td class="tg-8jgo">3.17</td>
+    <td class="tg-0u95"></td>
+    <td class="tg-km2t">(10) Herbaceous vegetation</td>
+    <td class="tg-8jgo">17.84</td>
+    <td class="tg-8jgo">19.76</td>
+    <td class="tg-x5zi"></td>
+    <td class="tg-km2t">(15) Clear cut</td>
+    <td class="tg-8jgo">0.15</td>
+    <td class="tg-8jgo">0.82</td>
+    <td class="tg-zv4m"></td>
+    <td class="tg-zv4m"></td>
+    <td class="tg-8jgo"></td>
+    <td class="tg-8jgo"></td>
+  </tr>
 </tbody>
 </table>
-</center>
-
-
 <br><br>
 
+## Dataset Structure
 
-## Usage 
+### Spatio-Temporal Distribution
+The FLAIR dataset consists of 77 762 patches. Each patch includes a high-resolution aerial image of $0.2$\:m (512x512), a yearly satellite image time series with a spatial resolution of 10m (40x40), and pixel-precise elevation and land cover annotations at 0.2m resolution (512x512).
 
-The `flair-2-config.yml` file controls paths, hyperparameters and computing ressources. The file `requirement.txt` is listing used libraries for the baselines.
+### Annotations
+Each pixel has been manually annotated by photo-interpretation of the 20cm resolution aerial imagery, carried out by a team supervised by geography experts from the IGN. 
+Movable objects like cars or boats are annotated according to their underlying cover.
 
-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>
+### Training Splits
+The dataset is made up of 50 distinct spatial domains, aligned with the administrative boundaries of the French départements. 
+For our experiments, we designate 32 domains for training, 8 for validation, and reserve 10 as the official test set. 
+This arrangement ensures a balanced distribution of semantic classes, radiometric attributes, bioclimatic conditions, and acquisition times across each set. 
+Consequently, every split accurately reflects the landscape diversity inherent to metropolitan France. 
+It is important to mention that the patches come with meta-data permitting alternative splitting schemes, for example focused on domain shifts.
 
 
 ## Reference
-Please include a citation to the following article if you use the FLAIR #2 dataset:
+Please include a citation to the following article if you use the FLAIR 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}
+@misc{garioud2023flair,
+      title={FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery}, 
+      author={Anatol Garioud and Nicolas Gonthier and Loic Landrieu and Apolline De Wit and Marion Valette and Marc Poupée and Sébastien Giordano and Boris Wattrelos},
+      year={2023},
+      eprint={2310.13336},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
 }
 ```