Update README.md

README.md

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 ## Uses
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+The model was specifically trained and designed for the segmentation of aerial lidar point clouds from the Lidar HD program (2020-2025), 
+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 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.
+
+**Data preprocessing**
+Point clouds were preprocessed for training with point subsampling, filtering of artefacts points, on-the-fly creation of colorimetric features, and normalization of features and coordinates. 
+For inference, the same preprocessing should be used (refer to the inference configuration and to the code repository).
+
+**Inference library: Myria3D**
+Model was trained in an open source deep learning code reposiroty developped in-house, and inference is only supported in this library. 
+The library comes with a Dockerfile as well as detailed documentation for inference. 
+Patched inference from large point clouds (e.g. 1 x 1 km Lidar HD tiles) is supported, with or without (default) overlapping sliding windows. 
+The original point cloud is augmented with several dimensions: a PredictedClassification dimension, an entropy dimension, and (optionnaly) class probability dimensions (e.g. building, ground...). 
+Refer to Myria3D's documentation for custom settings.
 
 ## Bias, Risks, Limitations and Recommendations