Update README.md

README.md

@@ -14,4 +14,169 @@ task_categories:
 pretty_name: FLAIR-HUB
 size_categories:
 - 100K<n<1M
----
+---
+
+# FLAIR-HUB : Large-scale Multimodal Dataset for Land Cover and Crop Mapping
+
+FLAIR-HUB builds upon and includes the FLAIR#1 and FLAIR#2 datasets, expanding them into a unified, large-scale, multi-sensor land-cover resource with very-high-resolution 
+annotations. Spanning over 2,500 km² of diverse French ecoclimates and landscapes, it features 63 billion hand-annotated pixels across 19 land-cover and 
+23 crop type classes.<br>
+The dataset integrates complementary data sources including aerial imagery, SPOT and Sentinel satellites, surface models, and historical aerial photos, 
+offering rich spatial, spectral, and temporal diversity. FLAIR-HUB supports the development of semantic segmentation, multimodal fusion, and self-supervised 
+learning methods, and will continue to grow with new modalities and annotations.
+
+
+## 🔗 Links
+
+<center>
+<table style="width:100%; max-width:700px; background-color:rgb(61, 60, 60); border: 2px solid green; border-radius: 12px; border-collapse: separate; border-spacing: 0;">
+  <tbody style="color: white; font-size: 1.05em;">
+    <tr><td style="border: none; padding: 4px 20px;">📄 <a href="https://arxiv.org/pdf/2211.12979.pdf" target="_blank" style="color: lightgreen;"><b>Data Paper</b></a> – Learn more about the dataset in the official publication</td></tr>
+    <tr><td style="border: none; padding: 4px 20px;">💻 <a href="https://github.com/IGNF/FLAIR-HUB" target="_blank" style="color: lightgreen;"><b>Source Code (GitHub)</b></a> – Explore training, preprocessing, and benchmark scripts</td></tr>
+    <tr><td style="border: none; padding: 4px 20px;">✉️ <a href="mailto:flair@ign.fr" style="color: lightgreen;"><b>Contact Us</b></a> – flair@ign.fr – Questions or collaboration inquiries welcome!</td></tr>
+  </tbody>
+</table>
+</center>
+
+<br>
+
+
+## 🎯 Key Figures
+
+<center>
+<table>
+  <tr><td>🗺️</td><td>ROI / Area Covered</td><td>➡️ 2,822 ROIs / 2,528 km²</td></tr>
+  <tr><td>🧠</td><td>Modalities</td><td>➡️ 6 modalities</td></tr>
+  <tr><td>🏛️</td><td>Departments (France)</td><td>➡️ 74</td></tr>
+  <tr><td>🧩</td><td>AI Patches (512×512 px @ 0.2m)</td><td>➡️ 241,100</td></tr>
+  <tr><td>🖼️</td><td>Annotated Pixels</td><td>➡️ 63.2 billion</td></tr>
+  <tr><td>🛰️</td><td>Sentinel-2 Acquisitions</td><td>➡️ 256,221</td></tr>
+  <tr><td>📡</td><td>Sentinel-1 Acquisitions</td><td>➡️ 532,696</td></tr>
+  <tr><td>📁</td><td>Total Files</td><td>➡️ ~2.5 million</td></tr>
+  <tr><td>💾</td><td>Total Dataset Size</td><td>➡️ ~750 GB</td></tr>
+</table>
+</center>
+
+
+
+### 🗂️ Data Modalities Overview
+
+<center>
+<table>
+  <thead>
+    <tr>
+      <th>Modality</th>
+      <th>Description</th>
+      <th style="text-align: center">Resolution / Format</th>
+      <th style="text-align: center">Metadata</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>BD ORTHO (AERIAL_RGBI)</strong></td>
+      <td>Orthorectified aerial images with 4 bands (R, G, B, NIR).</td>
+      <td style="text-align: center">20 cm, 8-bit unsigned</td>
+      <td style="text-align: center">Radiometric stats, acquisition dates/cameras</td>
+    </tr>
+    <tr>
+      <td><strong>BD ORTHO HISTORIQUE (AERIAL-RLT_PAN)</strong></td>
+      <td>Historical panchromatic aerial images (1947–1965), resampled.</td>
+      <td style="text-align: center">~40 cm, real: 0.4–1.2 m, 8-bit</td>
+      <td style="text-align: center">Dates, original image references</td>
+    </tr>
+    <tr>
+      <td><strong>ELEVATION (DEM_ELEV)</strong></td>
+      <td>Elevation data with DSM (surface) and DTM (terrain) channels.</td>
+      <td style="text-align: center">DSM: 20 cm, DTM: 1 m, Float32</td>
+      <td style="text-align: center">Object heights via DSM–DTM difference</td>
+    </tr>
+    <tr>
+      <td><strong>SPOT (SPOT_RGBI)</strong></td>
+      <td>SPOT 6-7 satellite images, 4 bands, calibrated reflectance.</td>
+      <td style="text-align: center">1.6 m (resampled)</td>
+      <td style="text-align: center">Acquisition dates, radiometric stats</td>
+    </tr>
+    <tr>
+      <td><strong>SENTINEL-2 (SENTINEL2_TS)</strong></td>
+      <td>Annual time series with 10 spectral bands, calibrated reflectance.</td>
+      <td style="text-align: center">10.24 m (resampled)</td>
+      <td style="text-align: center">Dates, radiometric stats, cloud/snow masks</td>
+    </tr>
+    <tr>
+      <td><strong>SENTINEL-1 ASC/DESC (SENTINEL1-XXX_TS)</strong></td>
+      <td>Radar time series (VV, VH), SAR backscatter (σ0).</td>
+      <td style="text-align: center">10.24 m (resampled)</td>
+      <td style="text-align: center">Stats per ascending/descending series</td>
+    </tr>
+    <tr>
+      <td><strong>LABELS CoSIA (AERIAL_LABEL-COSIA)</strong></td>
+      <td>Land cover labels from aerial RGBI photo-interpretation.</td>
+      <td style="text-align: center">20 cm, 15–19 classes</td>
+      <td style="text-align: center">Aligned with BD ORTHO, patch statistics</td>
+    </tr>
+    <tr>
+      <td><strong>LABELS LPIS (ALL_LABEL-LPIS)</strong></td>
+      <td>Crop type data from CAP declarations, hierarchical class structure.</td>
+      <td style="text-align: center">20 cm</td>
+      <td style="text-align: center">Aligned with BD ORTHO, may differ from CoSIA</td>
+    </tr>
+  </tbody>
+</table>
+</center>
+
+
+
+
+<br><p align="center"><img src="img/FLAIR-INC_patches_1_VERTICAL.png" alt="" style="width:100%;max-width:1300px;" /></p>
+
+
+
+
+<br><hr><br><a id="SUPERVISION"></a>
+
+### 🏷️ Supervision 
+
+FLAIR-HUB includes two complementary supervision sources: AERIAL_LABEL-COSIA, a high-resolution land cover annotation derived from expert photo-interpretation 
+of RGBI imagery, offering pixel-level precision across 19 classes; and AERIAL_LABEL-LPIS, a crop-type annotation based on farmer-declared parcels 
+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>
+
+
+
+
+
+### 📚 How to Cite
+
+If you use FLAIR-HUB in your research, please cite:
+
+```
+Anatol Garioud, Sébastien Giordano, Nicolas David, Nicolas Gonthier. 
+FLAIR-HUB: semantic segmentation and domain adaptation dataset. (2025). 
+DOI: https://doi.org/10.13140/RG.2.2.30183.73128/1
+```
+
+```bibtex
+@article{ign2025flairhub,
+  doi = {10.13140/RG.2.2.30183.73128/1},
+  url = {https://arxiv.org/pdf/2211.12979.pdf},
+  author = {Garioud, Anatol and Giordano, Sébastien and David, Nicolas and Gonthier, Nicolas},
+  title = {FLAIR #1: semantic segmentation and domain adaptation dataset},
+  publisher = {arXiv},
+  year = {2025}
+}
+```