Update README.md

README.md

@@ -32,10 +32,6 @@ tags:
   Lidar and imagery data were acquired over several years in distinct programs, and up to 3 years might separate them. The years of acquisition are given as metadata.  
 
 
-VHR Aerial imagery (ORTHO HR)          |   ALS points clouds (Lidar HD)
-:-------------------------:|:-------------------------:
-![](./imagery_18_classes.png)  |  ![](./lidar_18_classes.png)
-
 ## Dataset content
 <hr style='margin-top:-1em; margin-bottom:0' />
 The PureForest dataset consists of a total of 135,569 patches: 69111 in the train set, 13523 in the val set, and 52935 in the test set.
@@ -43,6 +39,10 @@ Each patch includes a high-resolution aerial image (250 pixels x 250 pixels) at
 Band order is near-infrared, red, greeb, blue. For convenience, the Lidar point clouds are vertically colorized with the aerial images.
 
 
+VHR Aerial images (Near-Infrared, Red, Green) [ORTHO HR]       |   ALS points clouds [Lidar HD]
+:-------------------------:|:-------------------------:
+![](./imagery_18_classes.png)  |  ![](./lidar_18_classes.png)
+
 ### Annotations
 <hr style='margin-top:-1em; margin-bottom:0' />
 Annotations were made at the forest level, and considering only monospecific forests. A semi-automatic approach was adopted in which forest polygons
@@ -68,15 +68,12 @@ were also used to improve the condidence in the purity of the forests.
 
 ### Dataset extent and train/val/test split
 <hr style='margin-top:-1em; margin-bottom:0' />
-The polygons were sampled in southern France due to the partial availability of the Lidar data at the time of dataset creation. 
-They are located in 40 distinct French administrative departments, covering a large diversity of territories and forests.
-To define a common benchmark, we divided the data into train, val, and test sets, with a stratification on semantic labels. 
-Annotation polygons are scattered across southern France, leading to a good geographical diversity within each semantic class. 
-To account for the high spatial autocorrelation, the 70%-15%-15% split is performed at the annotation polygon level: 
-each forest exclusively belongs to either the train, val, or test set.  
-This makes PureForest suitable to evaluate the territorial generalization of classification models. 
+The annotation polygons were mostly sampled in the southern half of metropolitan France due to the partial availability of the Lidar HD data at the time of dataset creation. 
+They are scattered in 40 distinct French administrative departments and span a large diversity of territories and forests within each semantic class.
 
-Approximate positions of forests in PureForest:
+To define a common benchmark, we split the data into train, val, and test sets (70%-15%-15%) with stratification on semantic labels. 
+We address the high spatial autocorrelation inherent to geographic data by splitting at the annotation polygon level: 
+each forest exclusively belongs to either the train, val, or test set.
 
 ![](./dataset_extent_map.excalidraw.png)
 
@@ -85,14 +82,14 @@ Approximate positions of forests in PureForest:
 Please include a citation to the following Data Paper if PureForest was useful to your research:
 
 ```
-@article{gaydon2024pureforest,
+@misc{gaydon2024pureforest,
       title={PureForest: A Large-scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests}, 
-      author={Gaydon, Charles and Roche, Floryne},
+      author={Charles Gaydon and Floryne Roche},
       year={2024},
-      publisher = {ArXiv},
+      eprint={2404.12064},
+      archivePrefix={arXiv},
       url={https://arxiv.org/abs/2404.12064}
-      doi={TBD},
-      language = {en},
+      primaryClass={cs.CV}
 }
 ```