Update README.md

README.md

@@ -18,13 +18,15 @@ tags:
 
 ## Context & Data
 <hr style='margin-top:-1em; margin-bottom:0' />
-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). 
+The hereby FLAIR (#1 and #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 satellite 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. This dataset provides a robust foundation for advancing land cover mapping techniques.<br><br>
+The dataset covers 55 distinct spatial domains, encompassing 974 areas spanning 980 km². This dataset provides a robust foundation for advancing land cover mapping techniques.
+We sample two test sets based on different input data and focus on semantic classes. The first test set (flair#1-test) uses very high resolution aerial imagery only and samples primarily anthropized land cover classes. 
+In contrast, the second test set (flair#2-test) combines aerial and satellite imagery and has more natural classes with temporal variations represented.<br><br>
 
 <style type="text/css">
 .tg  {border-collapse:collapse;border-spacing:0;}
@@ -61,11 +63,13 @@ The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km².
     <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-8jgo">Freq.-test flair#1 (%)</th>
+    <th class="tg-8jgo">Freq.-test flair#2 (%)</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-8jgo">Freq.-test flair#1 (%)</th>
+    <th class="tg-8jgo">Freq.-test flair#2 (%)</th>   
   </tr>
 </thead>
 <tbody>
@@ -73,96 +77,115 @@ The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km².
     <td class="tg-2e1p"></td>
     <td class="tg-km2t">(1) Building</td>
     <td class="tg-8jgo">8.14</td>
+    <td class="tg-8jgo">8.6</td>   
     <td class="tg-8jgo">3.26</td>
     <td class="tg-l5fa"></td>
     <td class="tg-km2t">(11) Agricultural Land</td>
     <td class="tg-8jgo">10.98</td>
+    <td class="tg-8jgo">6.95</td>    
     <td class="tg-8jgo">18.19</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">7.34</td>    
     <td class="tg-8jgo">3.82</td>
     <td class="tg-rime"></td>
     <td class="tg-km2t">(12) Plowed land</td>
     <td class="tg-8jgo">3.88</td>
+    <td class="tg-8jgo">2.25</td>    
     <td class="tg-8jgo">1.81</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">14.98</td>    
     <td class="tg-8jgo">5.87</td>
     <td class="tg-2cns"></td>
     <td class="tg-km2t">(13) Swimming pool</td>
     <td class="tg-8jgo">0.01</td>
+    <td class="tg-8jgo">0.04</td>    
     <td class="tg-8jgo">0.02</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">4.36</td>    
     <td class="tg-8jgo">1.6</td>
     <td class="tg-jjsp"></td>
     <td class="tg-km2t">(14) Snow</td>
     <td class="tg-8jgo">0.15</td>
+    <td class="tg-8jgo">-</td>    
     <td class="tg-8jgo">-</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">5.98</td>    
     <td class="tg-8jgo">3.17</td>
     <td class="tg-2w6m"></td>
     <td class="tg-km2t">(15) Clear cut</td>
     <td class="tg-8jgo">0.15</td>
+    <td class="tg-8jgo">0.01</td>    
     <td class="tg-8jgo">0.82</td>
   </tr>
   <tr>
     <td class="tg-b45e"></td>
     <td class="tg-km2t">(6) Coniferous</td>
     <td class="tg-8jgo">2.74</td>
+    <td class="tg-8jgo">2.39</td>
     <td class="tg-8jgo">10.24</td>
     <td class="tg-nla7"></td>
     <td class="tg-km2t">(16) Mixed</td>
     <td class="tg-8jgo">0.05</td>
+    <td class="tg-8jgo">-</td>    
     <td class="tg-8jgo">0.12</td>
   </tr>
   <tr>
     <td class="tg-qg2z"></td>
     <td class="tg-km2t">(7) Deciduous</td>
     <td class="tg-8jgo">15.38</td>
+    <td class="tg-8jgo">13.91</td>
     <td class="tg-8jgo">24.79</td>
     <td class="tg-nv8o"></td>
     <td class="tg-km2t">(17) Ligneous</td>
     <td class="tg-8jgo">0.01</td>
+    <td class="tg-8jgo">0.03</td>
     <td class="tg-8jgo">-</td>
   </tr>
   <tr>
     <td class="tg-grz5"></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-8jgo">6.91</td>
+    <td class="tg-8jgo">3.81</td>    
     <td class="tg-bja1"></td>
     <td class="tg-km2t">(18) Greenhouse</td>
     <td class="tg-8jgo">0.12</td>
+    <td class="tg-8jgo">0.2</td>    
     <td class="tg-8jgo">0.15</td>
   </tr>
   <tr>
     <td class="tg-69kt"></td>
     <td class="tg-km2t">(9) Vineyard</td>
     <td class="tg-8jgo">3.13</td>
+    <td class="tg-8jgo">3.87</td>
     <td class="tg-8jgo">2.55</td>
     <td class="tg-nto1"></td>
     <td class="tg-km2t">(19) Other</td>
     <td class="tg-8jgo">0.14</td>
+    <td class="tg-8jgo">0.-</td>    
     <td class="tg-8jgo">0.04</td>
   </tr>
   <tr>
     <td class="tg-r1r4"></td>
     <td class="tg-km2t">(10) Herbaceous vegetation</td>
     <td class="tg-8jgo">17.84</td>
+    <td class="tg-8jgo">22.17</td>
     <td class="tg-8jgo">19.76</td>
     <td class="tg-zv4m"></td>
     <td class="tg-zv4m"></td>
@@ -177,8 +200,10 @@ The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km².
 
 ## Dataset Structure
 <hr style='margin-top:-1em; margin-bottom:0' />
-The FLAIR dataset consists of 77 762 patches. Each patch includes a high-resolution aerial image (512x512) at 0.2 m, a yearly satellite image time series (40x40 by default by wider areas are provided) with a spatial resolution of 10 m 
-and associated cloud and snow masks, and pixel-precise elevation and land cover annotations at 0.2 m resolution (512x512).
+The FLAIR dataset consists of a total of 93 462 patches: 61 712 patches for the train/val dataset, 15 700 patches for flair#1-test and 16 050 patches for flair#2-test.
+
+Each patch includes a high-resolution aerial image (512x512) at 0.2 m, a yearly satellite image time series (40x40 by default by wider areas are provided) with a spatial resolution of 10 m 
+and associated cloud and snow masks (available in train/val and flair#2-test), and pixel-precise elevation and land cover annotations at 0.2 m resolution (512x512).
 
 <p align="center"><img src="readme_imgs/flair-patches.png" alt="" style="width:70%;max-width:600px;"/></p><br>
 
@@ -219,21 +244,23 @@ Satellite
 Each pixel has been manually annotated by photo-interpretation of the 20 cm 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.
 
-### 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. 
+### Data Splits
+The dataset is made up of 55 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 official test sets for flair#1-test and flair#2-test. 
+It can also be noted that some domains are common in the flair#1-test and flair#2-test datasets but cover different areas within the domain.  
 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.
+It is important to mention that the patches come with meta-data permitting alternative splitting schemes. 
+
 
 Official domain split: <br/>
 
 <div style="display: flex; flex-wrap: nowrap; align-items: center">
     <div style="flex: 40%;">
-        <img src="readme_imgs/flair-splits.png" alt="Your Image">
+        <img src="readme_imgs/flair-splits.png" alt="flair-splits">
 </div>
 
-  <div style="flex: 60%;">
+  <div style="flex: 60%; margin: auto;"">
   <table border="1">
     <tr>
       <th><font color="#c7254e">TRAIN:</font></th>
@@ -244,24 +271,53 @@ Official domain split: <br/>
       <td>D004, D014, D029, D031, D058, D066, D067, D077</td>
     </tr>
     <tr>
-      <th><font color="#c7254e">TEST:</font></th>
-      <td>D015, D022, D026, D036, D061, D064, D068, D069, D071, D084</td>
+      <th><font color="#c7254e">TEST-flair#1:</font></th>
+      <td>D012, D022, D026, D064, D068, D071, D075, D076, D083, D085</td>
     </tr>
+    <tr>
+      <th><font color="#c7254e">TEST-flair#2:</font></th>
+      <td>D015, D022, D026, D036, D061, D064, D068, D069, D071, D084</td>
+    </tr>    
   </table>
-
   </div>
 </div>
 
 <br><br>
 
+
 ## Baseline code 
 <hr style='margin-top:-1em; margin-bottom:0' />
+<br>
+
+### Flair #1 (aerial only)
+A U-Net architecture with a pre-trained ResNet34 encoder from the pytorch segmentation models library is used for the baselines. 
+The used architecture allows integration of patch-wise metadata information and employs commonly used image data augmentation techniques. 
+
+Flair#1 code repository &#128193; : https://github.com/IGNF/FLAIR-1<br/>
+Link to the paper : https://arxiv.org/pdf/2211.12979.pdf <br>
+
+Please include a citation to the following article if you use the FLAIR#1 dataset:
+
+```
+@article{ign2022flair1,
+  doi = {10.13140/RG.2.2.30183.73128/1},
+  url = {https://arxiv.org/pdf/2211.12979.pdf},
+  author = {Garioud, Anatol and Peillet, Stéphane and Bookjans, Eva and Giordano, Sébastien and Wattrelos, Boris},
+  title = {FLAIR #1: semantic segmentation and domain adaptation dataset},
+  publisher = {arXiv},
+  year = {2022}
+}
+```
+<br>
+
+### Flair #2 (aerial and satellite)
 We propose the U-T&T model, a two-branch architecture that combines spatial and temporal information from very high-resolution aerial images and high-resolution satellite images into a single output. The U-Net architecture is employed for the spatial/texture branch, using a ResNet34 backbone model pre-trained on ImageNet. For the spatio-temporal branch, 
 the U-TAE architecture incorporates a Temporal self-Attention Encoder (TAE) to explore the spatial and temporal characteristics of the Sentinel-2 time series data, 
 applying attention masks at different resolutions during decoding. This model allows for the fusion of learned information from both sources, 
 enhancing the representation of mono-date and time series data.
 
-U-T&T code repository &#128193; : https://github.com/IGNF/FLAIR-2-AI-Challenge <br/>
+U-T&T code repository &#128193; : https://github.com/IGNF/FLAIR-2<br/>
+Link to the paper : https://arxiv.org/abs/2310.13336 <br>
 
 <th><font color="#c7254e"><b>IMPORTANT!</b></font></th> <b>The structure of the current dataset differs from the one that comes with the GitHub repository.</b> 
 To work with the current dataset, you need to replace the <font color=‘#D7881C’><em>src/load_data.py</em></font> file with the one provided here. 
@@ -273,14 +329,8 @@ domains_train : ["D006_2020","D007_2020","D008_2019","D009_2019","D013_2020","D0
 domains_val : ["D004_2021","D014_2020","D029_2021","D031_2019","D058_2020","D066_2021","D067_2021","D077_2021"]   
 domains_test : ["D015_2020","D022_2021","D026_2020","D036_2020","D061_2020","D064_2021","D068_2021","D069_2020","D071_2020","D084_2021"]
 ```
-
-
-<br><br>
-
-
-## Reference
-<hr style='margin-top:-1em; margin-bottom:0' />
-Please include a citation to the following article if you use the FLAIR dataset:
+<br>
+Please include a citation to the following article if you use the FLAIR#2 dataset:
 
 ```
 @inproceedings{garioud2023flair,
@@ -291,8 +341,28 @@ Please include a citation to the following article if you use the FLAIR dataset:
       doi={https://doi.org/10.48550/arXiv.2310.13336},
 }
 ```
+<br>
 
-Link to the paper : https://arxiv.org/abs/2310.13336 <br>
+## CodaLab challenges
+<hr style='margin-top:-1em; margin-bottom:0' />
+
+The FLAIR dataset was used for two challenges organized by IGN in 2023 on the CodaLab platform.<br>
+Challenge FLAIR#1 : https://codalab.lisn.upsaclay.fr/competitions/8769 <br>
+Challenge FLAIR#2 : https://codalab.lisn.upsaclay.fr/competitions/13447 <br>
+<br>
+
+flair#1-test | The podium:  
+🥇 businiao - 0.65920  
+🥈 Breizhchess - 0.65600  
+🥉 wangzhiyu918 - 0.64930  
+
+flair#2-test | The podium:
+The podium:  
+🥇 strakajk - 0.64130  
+🥈 Breizhchess - 0.63550  
+🥉 qwerty64 - 0.63510  
+
+<br>
 
 ## Acknowledgment
 <hr style='margin-top:-1em; margin-bottom:0' />