Update README.md

README.md

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 # Datset Card for FLAIR land-cover semantic segmentation
 
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 ## Context & Data
 
 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). 
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 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.
 
+<p align="center"><img src="flair-splits.png" alt="" style="width:50%;max-width:600px;"/></p>
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+<br><br>
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+## Baseline code 
+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.
 
-<p align="center"><img src="flair-splits.png" alt="" style="width:50%;max-width:600px;"/></p><br>
+U-T&T code repository &#128193; : https://github.com/IGNF/FLAIR-2-AI-Challenge <br/>
 
+<br><br>
 
 
 ## Reference