Update README.md

README.md

@@ -20,17 +20,18 @@ tags:
 
 # Dataset Card for PureForest
 
-## _PureForest_ in a nutshell
-
-- Data from 449 distinct closed forests in 40, mostly southern, French departments
-- 135,569 patches (50m*50m), totalling 339km² of exploitable data.
-- Two modalities:
-    - High density aerial Lidar point clouds (10 pulse/m²) totalling 118 Go, in LAZ format.
-    - High resolution aerial images (250x250p at 0.2 m spatial resolution) totalling  34 Go, in GeoTIFF format.
-- Patches of pure species forest, with a single tree species label, for classification tasks.
-- 13 semantic classes, hierarchically grouping 18 tree species from 9 tree genus.
-- A reference train/val/test split with class stratification
-- Data for each forest exclusively belongs to either the train, val, or test set.
+## Context and Data
+
+The hereby PureForest dataset is derived from 449 different forests located in 40 French departments, mainly in the southern regions. 
+It is characterized by two modalities: high density aerial Lidar point clouds with a density of 10 pulses per square meter, 
+and high resolution aerial imagery with a spatial resolution of 0.2 m.
+
+This dataset includes 135,569 patches, each measuring 50m*50m, covering a cumulative exploitable area of 339km². 
+Each patch represents a monospecific forest, labeled with a single tree species to facilitate classification tasks. 
+The proposed classification features 13 semantic classes, hierarchically grouping 18 tree species from 9 different tree genus.
+
+A reference train/val/test split is provided with class stratification. 
+To account for spatial autocorrelation, each forest exclusively belongs to either the train, validation, or test set.
 
 | Class | Train (%) | Val (%) | Test (%) |
 |-------|------------:|----------:|-----------:|
@@ -48,16 +49,30 @@ tags:
 **(11) Larch**|3.67%|3.73%|0.48%
 **(12) Douglas**|0.23%|1.95%|0.20%
 
-## Dataset structure
+## Dataset Structure
 
 The PureForest dataset consists of a total of 135,569 patches: 69111 in the train set, 13523 in the validation set, and 52935 in the test set.
 Each patch includes a high-resolution aerial image (250x250) at 0.2 m resolution, and a point cloud of high density aerial Lidar (10 pulses/m², ~40pts/m²).
-Band order is Near Infrared, Red, Green, Blue. for convenience, the Lidar point clouds are vertically colorized with the aerial images.
+Band order is Near Infrared, Red, Green, Blue. For convenience, the Lidar point clouds are vertically colorized with the aerial images.
+
+Lidar and imagery data were acquired over several years in distinct programs, and consequently they are asynchrone: depending on the location, up to 3 years might separate them. 
 
 Lidar points clouds            |  Aerial imagery
 :-------------------------:|:-------------------------:
 ![](./imagery_18_classes.png)  |  ![](./lidar_18_classes.png)
 
+## Annotation
+
+Annotation were made at the forest level, and considering only monospecific forests. A semi-automatic approach was adopted in which pure forest polygons
+were selected and then curated by photointerpreters. The annotation polygons came from the [BD Forêt](https://inventaire-forestier.ign.fr/spip.php?article646), 
+a forest vector database of tree species occupation in France. Ground truths from the F[rench National Forest Inventory](https://inventaire-forestier.ign.fr/?lang=en) 
+were also used.
+
+## Data Splits
+
+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. 
+
 
 ## Citation
 Please include a citation to the following article if you use the PureForest dataset: