Update README.md

README.md

@@ -78,9 +78,9 @@ pipeline_tag: image-segmentation
 ---
 
 # FLAIR model collection
-The FLAIR models is a collection of semantic segmentation models initially developed to classify land cover on very high resolution aerial ortho-images ([BD ORTHO®](https://geoservices.ign.fr/bdortho)). 
+The FLAIR models are a collection of semantic segmentation models initially developed to classify land cover on very high resolution aerial images (more specifically the French [BD ORTHO®](https://geoservices.ign.fr/bdortho) product). 
 The distributed pre-trained models differ in their :
-* dataset for training : [**FLAIR** dataset] (https://huggingface.co/datasets/IGNF/FLAIR) or the increased version of this dataset **FLAIR-INC** (x 3.5 patches)  .
+* dataset for training : [**FLAIR** dataset] (https://huggingface.co/datasets/IGNF/FLAIR) or the increased version of this dataset **FLAIR-INC** (x 3.5 patches). Only the FLAIR version is open at the moment.
 * input modalities : **RGB** (natural colours), **RGBI** (natural colours + infrared), **RGBIE** (natural colours + infrared + elevation)
 * model architecture : **resnet34_unet** (U-Net with a Resnet-34 encoder), **deeplab**
 * target class nomenclature : **12cl** (12 land cover classes) or **15cl** (15 land cover classes) 
@@ -89,6 +89,7 @@ The distributed pre-trained models differ in their :
 # FLAIR FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model
 
 The general characteristics of this specific model **FLAIR-INC_RVBIE_resnet34_unet_15cl_norm** are :
+* trained with the FLAIR-INC dataset 
 * RGBIE images  (true colours + infrared + elevation)
 * U-Net with a Resnet-34 encoder
 * 15 class nomenclature : [building, pervious_ surface, impervious_surface, bare_soil, water, coniferous, deciduous, brushwood, vineyard, herbaceous, agricultural_land, plowed_land, swimming pool, snow, greenhouse]
@@ -122,6 +123,11 @@ _**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 frequent and are mainly due to : the date of acquisition of the aerial survey (april to november), the spatial domain (equivalent to a french department administrative division) and downstream radimetric processing.
 By construction (sampling 75 domains) the model is robust to these shifts, and can be applied to any images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)).
 
+_**Specification for the Elevation channel**_ :
+The fifth dimension of the RGBIE images is the Elevation (height of building and vegetation). This information is encoded in a 8-bit encoding format. 
+When decoded to [0,255] ints, a difference of 1 coresponds to 20 cm step of elevation difference.   
+
+
 _**Land Cover classes of prediction**_ :
 The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes(See the [FLAIR dataset](https://huggingface.co/datasets/IGNF/FLAIR) page for details).
 However 3 classes corresponding to uncertain labelisation (Mixed (16), Ligneous (17) and Other (19)) and 1 class with very poor labelling (Clear cut (15)) were deasctivated during training.
@@ -294,6 +300,8 @@ The following illustration give the confusion matrix :
 
 ### Results
 
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 #### Summary
@@ -314,10 +322,19 @@ The following illustration give the confusion matrix :
 
 **BibTeX:**
 
+@misc{garioud2023flair,
+      title={FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery}, 
+      author={Anatol Garioud and Nicolas Gonthier and Loic Landrieu and Apolline De Wit and Marion Valette and Marc Poupée and Sébastien Giordano and Boris Wattrelos},
+      year={2023},
+      eprint={2310.13336},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
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 **APA:**
-
+Garioud, A., Gonthier, N., Landrieu, L., De Wit, A., Valette, M., Poupée, M., ... & Wattrelos, B. (2023). FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery. arXiv preprint arXiv:2310.13336.
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