Update README.md

README.md

@@ -27,17 +27,17 @@ The hereby PureForest dataset is derived from 449 different forests located in 4
 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. 
+This dataset includes 135,569 patches, each measuring 50 m x 50 m, covering a cumulative exploitable area of 339 km². 
+Each patch represents a monospecific forest, annotated with a single tree species labeled to facilitate classification tasks. 
+The proposed classification has 13 semantic classes, hierarchically grouping 18 tree species from 9 different tree genera. 
 
 A reference train/val/test split is provided.
 
 ## Dataset Structure
 <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 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.
+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.
+Each patch includes a high-resolution aerial image (250 pixels x 250 pixels) 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, greeb, 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. 
 
@@ -47,9 +47,9 @@ Lidar points clouds            |  Aerial imagery
 
 ## Annotations
 <hr style='margin-top:-1em; margin-bottom:0' />
-Annotation were made at the forest level, and considering only monospecific forests. A semi-automatic approach was adopted in which pure forest polygons
+Annotations 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 expert photointerpreters from the IGN. 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) 
+a forest vector database of tree species occupation in France. Ground truths from the [French National Forest Inventory](https://inventaire-forestier.ign.fr/?lang=en) 
 were also used to improve the condidence in the purity of the forests.
 
 | Class | Train (%) | Val (%) | Test (%) |
@@ -72,14 +72,13 @@ were also used to improve the condidence in the purity of the forests.
 <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, validation, and test sets, with a stratification on semantic labels. 
+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 split is performed at the annotation polygon level: 
-each forest exclusively belongs to either the train, validation, or test set.  
+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. 
-We aimed for a 70%-15%-15% split across the train, validation, and test sets, respectively.
 
-Approximate positions of forests in PureForest
+Approximate positions of forests in PureForest:
 
 ![](./dataset_extent_map.excalidraw.png)
 
@@ -89,8 +88,8 @@ Please include a citation to the following article if you use the PureForest dat
 
 ```
 @article{gaydon2024pureforest,
-      title={PureForest: A New Benchmark Dataset for Tree Species Classification using Lidar and Aerial Imagery}, 
-      author={Charles Gaydon and Rémi Pas and Daniel Mijalcevic and Floryne Roche},
+      title={PureForest: A Novel Dataset and Benchmarks for Tree Species Classification in Monospecific Forests from Aerial Lidar and Aerial Images}, 
+      author={Charles Gaydon and Floryne Roche},
       year={2024},
       doi={TBD},
 }