Update README.md

README.md

@@ -115,33 +115,33 @@ The model has been trained with
 
 
 **Radiometry of input images** :
-The input images are distributed in 8-bit encoding format per channel. For traning the model, input normalization was performed so as the input dataset has a mean of 0 and a standart deviation of 1. 
-For this model here are the statistics of the TRAIN+VALIDATION partition. It is recommended that the user apply the same type of input normalization.
-
-| Modalities              | Mean        |Std         |
-| ----------------------- | ----------- |----------- |
-| Red Channel (R)         | 105.08	    |52.17       |
-| Green Channel (V)       | 110.87      |45.38       |
-| Blue Channel (B)        | 101.82	    |44.00       |
-| Infrared Channel (I)    | 106.38	    |39.69       |
-| Elevation Channel (E)   | 53.26       |79.30       |
+The input images are distributed in 8-bit encoding format per channel. or traning the model, input normalization was performed (see section **Traing Details**). 
+It is recommended that the user apply the same type of input normalization while inferring the model.
 
 
 **Multi-domain model** :
 The FLAIR-INC dataset that was used for training is composed of 75 radiometric domains. In the case of aerial images, domain shifts are due : the date of the aerial survey (april to november), spatial domain (equivalent to a french department administrative division) and downstream radimetric processing.
 By construction the model is robust to theses shifts, and can be applied to any images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)).
 
+**Land Cover classes of prediction** :
+
 
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-**Spatial resolution of input images** :
+**Using the model on input images with other spatial resolution** :
 The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with fixed scale conditions. All patches used for training are derived from aerial images of 0.2 meters spatial resolution. 
 No data augmentation method concerning scale change was used during training. The user should pay attention that generalization issues can occur while applying this model to images that have different spatial resolutions.
 
+**Using the model for other remote sensing sensors** :
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with aerial images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)) that encopass very specific radiometric image processing. 
+Using the model on other type of aerial images or satellite images may imply the use of transfer learning or domain adaptation techniques.
 
+**Using the model on other spatial areas** :
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained on patches reprensenting the French Metropolitan territory. 
+The user should be aware that applying the model to other type of landscapes may imply a drop in model metrics.