Update README.md

README.md

@@ -64,22 +64,14 @@ model-index:
 ---
 
 ## Uses
-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.
+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.
 
 **_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. 
-Myria3D 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 (by 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.
-
 **_Multi-domain model_**: The FRACTAL dataset used for training covers 5 spatial domains from 5 southern regions of metropolitan France. 
 The 250 km² of data in FRACTAL were sampled from an original 17440 km² area, and cover a wide diversity of landscapes and scenes. 
 While large and diverse, this data only covers a fraction of the French territory, and the model should be used with adequate verifications when applied to new domains.
@@ -93,9 +85,11 @@ the aerial lidar point clouds are expected to have more consistent characteristi
 
 ## How to Get Started with the Model
 
-Visit ([https://github.com/IGNF/FLAIR-1](https://github.com/IGNF/myria3d)) to use the model.
-Fine-tuning and prediction tasks are detailed in the README file.
-
+Model was trained in an open source deep learning code repository developped in-house: [github.com/IGNF/myria3d](https://github.com/IGNF/myria3d)). 
+Inference is only supported in this library, and inference instructions are detailed in the code repository documentation.
+Patched inference from large point clouds (e.g. 1 x 1 km Lidar HD tiles) is supported, with or without (by 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...). 
+For convenience and scalable model deployment, Myria3D comes with a Dockerfile.
 
 ---