Update README.md

README.md

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 ## Uses
-The model was specifically trained and designed for the **semantic segmentation of aerial lidar point clouds from the Lidar HD program (2020-2025)**. 
-The Lidar HD is an ambitious initiative that aim to obtain a 3D description of the French territory by 2026. 
-While the model could be applied to other types of point clouds, [Lidar HD](https://geoservices.ign.fr/lidarhd) data have specific geometric specifications. Furthermore, the training data was colorized 
-with very-high-definition aerial images from the ([BD ORTHO®](https://geoservices.ign.fr/bdortho)), which have their own spatial and radiometric specifications. Consequently, the model's prediction would improve for aerial lidar point clouds with similar densities and colorimetries than the original ones.
+The model was specifically trained for the **semantic segmentation of aerial lidar point clouds from the Lidar HD program (2020-2025)**.
 
-**_Data preprocessing_**: ?? keep ?
+**_Aerial Lidar scene understanding_**: the model is designed for the segmentation of aerial lidar point clouds into 7 classes: other | ground | vegetation | building | water | bridge | permanent structure.
+While the model could be applied to other types of point clouds (mobile, terrestrial), aerial lidar scanning has specific geometric specifications (occlusions, homogeneous densities, variable scanner angle...). 
+Furthermore, the aerial images used for point cloud colorization (from the ([BD ORTHO®](https://geoservices.ign.fr/bdortho)), have their own spatial and radiometric specifications. 
+Therefore, the model is best optimized for aerial lidar point clouds with similar densities and colorimetries than the original ones.
 
 
 ## Bias, Risks, Limitations and Recommendations
@@ -83,7 +83,6 @@ The data sources have their specificities in terms of resolution and spectral do
 This being said, while domain shifts are frequent for aerial imageries due to different acquisition conditions and downstream data processing, 
 aerial lidar point clouds of comparable point densities (~40 pts/m²) are expected to have more consistent geometric characteristiques across spatial domains.
 
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 ## How to Get Started with the Model