Update README.md

README.md

@@ -33,8 +33,8 @@ learning methods, and will continue to grow with new modalities and annotations.
 
 ## 🔗 Links
 
-📄 <a href="https://arxiv.org/pdf/2211.12979.pdf" target="_blank"><b>Data Paper</b></a> – Learn more about the dataset in the official publication<br>
-💻 <a href="https://github.com/IGNF/FLAIR-HUB" target="_blank"><b>Source Code (GitHub)</b></a> – Explore training, preprocessing, and benchmark scripts<br>
+📄 <a href="https://arxiv.org/pdf/2211.12979.pdf" target="_blank"><b>Data Paper</b></a><br>
+💻 <a href="https://github.com/IGNF/FLAIR-HUB" target="_blank"><b>Source Code (GitHub)</b></a><br>
 ✉️ <a href="mailto:flair@ign.fr"><b>Contact Us</b></a> – flair@ign.fr – Questions or collaboration inquiries welcome!<br>
 
 
@@ -132,20 +132,15 @@ of RGBI imagery, offering pixel-level precision across 19 classes; and AERIAL_LA
 from the European Common Agricultural Policy, structured into a three-level taxonomy of up to 46 crop classes. While COSIA reflects actual land cover, 
 LPIS captures declared land use, and the two differ in purpose, precision, and spatial alignment.
 
-<center>
-<table style="width:100%; max-width:1100px;">
-  <tr>
-    <td style="text-align:center;">
-      <img src="img/NOM_cosia_en.png" alt="Figure 1" width="80%">
-      <div><small>Land-cover supervision</small></div>
-    </td>
-    <td style="text-align:center;">
-      <img src="img/NOM_lpis_en.png" alt="Figure 2" width="87%">
-      <div><small>Crop-type supervision</small></div>
-    </td>
-  </tr>
-</table>
-</center>
+<p align="center"><img src="datacard_imgs/FLAIR-HUB_Labels.png" alt="" style="width:60%;max-width:1300px;" /></p>
+
+
+
+### 🌍 Spatial partition 
+
+
+
+
 
 
 ### 📚 How to Cite
@@ -167,3 +162,9 @@ DOI: https://doi.org/10.13140/RG.2.2.30183.73128/1
   year = {2025}
 }
 ```
+
+
+### ⚙️ Acknowledgement
+
+Experiments have been conducted using HPC/AI resources provided by GENCI-IDRIS (Grant 2024-A0161013803, 2024-AD011014286R2 and 2025-A0181013803).
+