Update README.md

README.md

@@ -26,7 +26,7 @@ Participate in obtaining more accurate maps for a more comprehensive description
 
 - **Datapaper : https://arxiv.org/pdf/2305.14467.pdf** 
 
-- **Dataset links :** https://ignf.github.io/FLAIR/#FLAIR2 [🛑 soon @ https://ignf.github.io/FLAIR/ !]
+- **Dataset links :** https://ignf.github.io/FLAIR/#FLAIR2
 
 - **Challenge page : https://codalab.lisn.upsaclay.fr/competitions/13447** 
 
@@ -42,31 +42,58 @@ Participate in obtaining more accurate maps for a more comprehensive description
 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>
 
-<p align="center">
-  <img width="40%" src="images/flair-2-spatial.png">
-  <br>
-  <em>Spatial definitions of the FLAIR #2 dataset.</em>
-</p>
+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;">
+<thead>
+  <tr><th width=7%></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>
 
-<p align="center">
-  <img width="85%" src="images/flair-2-patches.png">
-  <br>
-  <em>Example of input data (first three columns are from aerial imagery, fourth from Sentinel-2) and corresponding supervision masks (last column).</em>
-</p>
 
 <br><br>
-## Baseline model 
-
-A two-branch architecture integrating a U-Net <a href="https://github.com/qubvel/segmentation_models.pytorch"><img src="https://img.shields.io/badge/Link%20to-SMP-f4dbaa.svg"/></a> with a pre-trained ResNet34 encoder and a U-TAE <a href="https://github.com/VSainteuf/utae-paps"><img src="https://img.shields.io/badge/Link%20to-U--TAE-f4dbaa.svg"/></a> encompassing a temporal self-attention encoder is presented. The U-TAE branch aims at learning spatio-temporal embeddings from the high resolution satellite time series that are further integrated into the U-Net branch exploiting the aerial imagery. The proposed _U-T&T_ model features a fusion module to extend and reshape the U-TAE embeddings in order to add them towards the U-Net branch.   
 
-<p align="center">
-  <img width="100%" src="images/flair-2-network.png">
-  <br>
-  <em>Overview of the proposed two-branch architecture.</em>
-</p>
-
-<br><br>
 
 ## Usage 
 
@@ -84,32 +111,6 @@ A toy dataset (reduced size) is available to check that your installation and th
 
 <br><br>
 
-## Leaderboard
-
-Please note that for participants to the FLAIR #2 challenge on CodaLab, a certain number of constraints must be satisfied (in particular, inference time). All infos are available on the _Overview_ page of the competion.
-
-| Model|Input|mIoU 
------------- | ------------- | -------------
-| baseline U-Net (ResNet34) | aerial imagery | 0.5470
-| baseline U-Net (ResNet34) + _metadata + augmentation_ | aerial imagery | 0.5593
-|||
-| baseline U-T&T | aerial and satellite imagery | 0.5594
-| baseline U-T&T + _filter clouds + monthly averages + data augmentation_ | aerial and satellite imagery | 0.5758
-
-If you want to submit a new entry, you can open a new issue.
-<b> Results of the challenge will be reported after the end of the challenge early October! </b>
-
-The baseline U-T&T + _filter clouds + monthly averages + data_augmentation_ obtains the following confusion matrix: 
-
-<br><br>
-<p align="center">
-  <img width="50%" src="images/flair-2-confmat.png">
-  <br>
-  <em>Baseline confusion matrix of the test dataset normalized by rows.</em>
-</p>
-
-
-<br><br><br>
 
 ## Reference
 Please include a citation to the following article if you use the FLAIR #2 dataset: