Update README.md

README.md

@@ -114,15 +114,6 @@ The general characteristics of this specific model *FLAIR-INC_RVBIE_resnet34_une
 The model has been trained with
 
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-**Spatial resolution of input images** :
-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.
-
-
 **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.
@@ -136,6 +127,22 @@ For this model here are the statistics of the TRAIN+VALIDATION partition. It is
 | Elevation Channel (E)   | 53.26       |79.30       |
 
 
+**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)).
+
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+**Spatial resolution of input images** :
+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.
+
+
+