Update README.md

README.md

@@ -10,6 +10,7 @@ size_categories:
 # Datset Card for FLAIR land-cover semantic segmentation
 
 ## Context & Data
+<hr style='margin-top:-1em' />
 
 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). 
@@ -168,8 +169,8 @@ The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km².
 <br><br>
 
 ## Dataset Structure
+<hr style='margin-top:-1em' />
 
-### Spatio-Temporal Distribution
 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).
 
@@ -177,10 +178,36 @@ and associated cloud and snow masks, and pixel-precise elevation and land cover
 
 
 ### Band order
-Aerial : 1. Red;  2. Green; 3. Blue; 4. NIR; 5. nDSM <br/>
-Satellite : 1. Blue (B2 490nm); 2. Green (B3 560nm); 3. Red (B4 665nm); 4. Red-Edge (B5 705nm); 5. Red-Edge2 (B6 470nm); 
-6. Red-Edge3 (B7 783nm); 7. NIR (B8 842nm); 8. NIR-Red-Edge (B8a 865nm); 9. SWIR (B11 1610nm); 10. SWIR2 (B12 2190nm)
 
+<div style="display: flex;">
+<div style="width: 15%;margin-right: 1;"">
+Aerial
+<ul>
+<li>1. Red</li>
+<li>2. Green</li>
+<li>3. Blue</li>
+<li>4. NIR</li>
+<li>5. nDSM</li>
+</ul>
+</div>
+
+<div style="width: 25%;">
+Satellite
+<ul>
+<li>1. Blue (B2 490nm)</li>
+<li>2. Green (B3 560nm)</li>
+<li>3. Red (B4 665nm)</li>
+<li>4. Red-Edge (B5 705nm)</li>
+<li>5. Red-Edge2 (B6 470nm)</li>
+<li>6. Red-Edge3 (B7 783nm)</li>
+<li>7. NIR (B8 842nm)</li>
+<li>8. NIR-Red-Edge (B8a 865nm)</li>
+<li>9. SWIR (B11 1610nm)</li>
+<li>10. SWIR2 (B12 2190nm)</li>
+</ul>
+</div>
+
+</div>
 
 ### Annotations
 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. 
@@ -193,7 +220,7 @@ This arrangement ensures a balanced distribution of semantic classes, radiometri
 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.
 
-Official split: <br/>
+Official domain split: <br/>
 
 <div style="display: flex; flex-wrap: nowrap; align-items: center">
     <div style="flex: 40%;">
@@ -222,6 +249,8 @@ Official split: <br/>
 <br><br>
 
 ## Baseline code 
+<hr style='margin-top:-1em' />
+
 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, 
@@ -231,46 +260,48 @@ U-T&T code repository &#128193; : https://github.com/IGNF/FLAIR-2-AI-Challenge <
 
 <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. 
-You also need to add the following content to your flair-2-config.yml file under the <em><b>data</b></em> tag: <br>
-
-<code>
-  
-  HF_data_path : " " # Path to unzipped HF dataset  
-  
-  domains_train : ["D006_2020","D007_2020","D008_2019","D009_2019","D013_2020","D016_2020","D017_2018","D021_2020","D023_2020","D030_2021","D032_2019","D033_2021","D034_2021","D035_2020","D038_2021","D041_2021","D044_2020","D046_2019","D049_2020","D051_2019","D052_2019","D055_2018","D060_2021","D063_2019","D070_2020","D072_2019","D074_2020","D078_2021","D080_2021","D081_2020","D086_2020","D091_2021"]
+You also need to add the following content to the <font color=‘#D7881C’><em>flair-2-config.yml</em></font> file under the <em><b>data</b></em> tag: <br>
 
-  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"]
-
-</code>
+```
+HF_data_path : " " # Path to unzipped HF dataset
+domains_train : ["D006_2020","D007_2020","D008_2019","D009_2019","D013_2020","D016_2020","D017_2018","D021_2020","D023_2020","D030_2021","D032_2019","D033_2021","D034_2021","D035_2020","D038_2021","D041_2021","D044_2020","D046_2019","D049_2020","D051_2019","D052_2019","D055_2018","D060_2021","D063_2019","D070_2020","D072_2019","D074_2020","D078_2021","D080_2021","D081_2020","D086_2020","D091_2021"]
+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' />
+
 Please include a citation to the following article if you use the FLAIR dataset:
 
 ```
-@misc{garioud2023flair,
+@inproceedings{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}
+      booktitle={Advances in Neural Information Processing Systems (NeurIPS) 2023},
+      doi={https://doi.org/10.48550/arXiv.2310.13336},
 }
 ```
 
 ## Acknowledgment
+<hr style='margin-top:-1em' />
+
 This work was performed using HPC/AI resources from GENCI-IDRIS (Grant 2022-A0131013803). This work was supported by the project "Copernicus / FPCUP” of the European Union, by the French Space Agency (CNES) and by Connect by CNES.<br>
 
 
+## Contact
+<hr style='margin-top:-1em' />
 
+If you have any questions, issues or feedback, you can contact us at: ai-challenge@ign.fr
 
 
 ## Dataset license
+<hr style='margin-top:-1em' />
 
 The "OPEN LICENCE 2.0/LICENCE OUVERTE" is a license created by the French government specifically for the purpose of facilitating the dissemination of open data by public administration.<br/>
 This licence is governed by French law.<br/>