Update README.md

README.md

@@ -71,17 +71,22 @@ with very-high-definition aerial images from the ([BD ORTHO®](https://geoservic
 
 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. 
+**_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. 
+**_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.
+This being said, while domain shifts are frequent for aerial imageries due to different acquisition conditions and downstream data processing, 
+the aerial lidar point clouds are expected to have more consistent characteristiques 
+(density, range of acquisition angle, etc.) across spatial domains.
+
 ## Bias, Risks, Limitations and Recommendations
 
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