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@@ -19,37 +19,22 @@ tags:
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  ---
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  # Dataset Card for PureForest
 
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  ## Context and Data
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-
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  The hereby PureForest dataset is derived from 449 different forests located in 40 French departments, mainly in the southern regions.
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  It is characterized by two modalities: high density aerial Lidar point clouds with a density of 10 pulses per square meter,
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  and high resolution aerial imagery with a spatial resolution of 0.2 m.
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  This dataset includes 135,569 patches, each measuring 50m*50m, covering a cumulative exploitable area of 339km².
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  Each patch represents a monospecific forest, labeled with a single tree species to facilitate classification tasks.
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- The proposed classification features 13 semantic classes, hierarchically grouping 18 tree species from 9 different tree genus.
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  A reference train/val/test split is provided.
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- | Class | Train (%) | Val (%) | Test (%) |
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- |-------|------------:|----------:|-----------:|
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- **(0) Deciduous oak**|22.92%|32.35%|52.59%
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- **(1) Evergreen oak**|16.80%|2.75%|19.61%
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- **(2) Beech**|10.14%|12.03%|7.62%
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- **(3) Chestnut**|4.83%|1.09%|0.38%
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- **(4) Black locust**|2.41%|2.40%|0.60%
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- **(5) Maritime pine**|6.61%|7.10%|3.85%
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- **(6) Scotch pine**|16.39%|17.95%|8.51%
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- **(7) Black pine**|6.30%|6.98%|3.64%
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- **(8) Aleppo pine**|5.83%|1.72%|0.83%
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- **(9) Fir**|0.14%|5.32%|0.05%
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- **(10) Spruce**|3.73%|4.64%|1.64%
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- **(11) Larch**|3.67%|3.73%|0.48%
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- **(12) Douglas**|0.23%|1.95%|0.20%
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-
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  ## Dataset Structure
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-
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  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.
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  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).
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  Band order is Near Infrared, Red, Green, Blue. For convenience, the Lidar point clouds are vertically colorized with the aerial images.
@@ -60,15 +45,31 @@ Lidar points clouds | Aerial imagery
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  :-------------------------:|:-------------------------:
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  ![](./imagery_18_classes.png) | ![](./lidar_18_classes.png)
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- ## Annotation
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-
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  Annotation were made at the forest level, and considering only monospecific forests. A semi-automatic approach was adopted in which pure forest polygons
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  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),
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  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)
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  were also used to improve the condidence in the purity of the forests.
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- ## Data Splits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  The polygons were sampled in southern France due to the partial availability of the Lidar data at the time of dataset creation.
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  They are located in 40 distinct French administrative departments, covering a large diversity of territories and forests.
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  To define a common benchmark, we divided the data into train, validation, and test sets, with a stratification on semantic labels.
@@ -83,6 +84,7 @@ Approximate positions of forests in PureForest
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  ![](./dataset_extent_map.excalidraw.png)
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  ## Citation
 
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  Please include a citation to the following article if you use the PureForest dataset:
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  ```
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  @article{gaydon2024pureforest,
@@ -92,7 +94,7 @@ Please include a citation to the following article if you use the PureForest dat
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  doi={TBD},
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  }
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  ```
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- <br>
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  ## Dataset license
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  <hr style='margin-top:-1em; margin-bottom:0' />
 
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  ---
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  # Dataset Card for PureForest
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+ <hr style='margin-top:-1em; margin-bottom:0' />
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  ## Context and Data
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+ <hr style='margin-top:-1em; margin-bottom:0' />
26
  The hereby PureForest dataset is derived from 449 different forests located in 40 French departments, mainly in the southern regions.
27
  It is characterized by two modalities: high density aerial Lidar point clouds with a density of 10 pulses per square meter,
28
  and high resolution aerial imagery with a spatial resolution of 0.2 m.
29
 
30
  This dataset includes 135,569 patches, each measuring 50m*50m, covering a cumulative exploitable area of 339km².
31
  Each patch represents a monospecific forest, labeled with a single tree species to facilitate classification tasks.
32
+ The proposed classification features 13 semantic classes, hierarchically grouping 18 tree species from 9 different tree genus.
33
 
34
  A reference train/val/test split is provided.
35
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
36
  ## Dataset Structure
37
+ <hr style='margin-top:-1em; margin-bottom:0' />
38
  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.
39
  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).
40
  Band order is Near Infrared, Red, Green, Blue. For convenience, the Lidar point clouds are vertically colorized with the aerial images.
 
45
  :-------------------------:|:-------------------------:
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  ![](./imagery_18_classes.png) | ![](./lidar_18_classes.png)
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+ ## Annotations
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+ <hr style='margin-top:-1em; margin-bottom:0' />
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  Annotation were made at the forest level, and considering only monospecific forests. A semi-automatic approach was adopted in which pure forest polygons
51
  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),
52
  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)
53
  were also used to improve the condidence in the purity of the forests.
54
 
55
+ | Class | Train (%) | Val (%) | Test (%) |
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+ |-------|------------:|----------:|-----------:|
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+ **(0) Deciduous oak**|22.92%|32.35%|52.59%
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+ **(1) Evergreen oak**|16.80%|2.75%|19.61%
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+ **(2) Beech**|10.14%|12.03%|7.62%
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+ **(3) Chestnut**|4.83%|1.09%|0.38%
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+ **(4) Black locust**|2.41%|2.40%|0.60%
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+ **(5) Maritime pine**|6.61%|7.10%|3.85%
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+ **(6) Scotch pine**|16.39%|17.95%|8.51%
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+ **(7) Black pine**|6.30%|6.98%|3.64%
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+ **(8) Aleppo pine**|5.83%|1.72%|0.83%
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+ **(9) Fir**|0.14%|5.32%|0.05%
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+ **(10) Spruce**|3.73%|4.64%|1.64%
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+ **(11) Larch**|3.67%|3.73%|0.48%
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+ **(12) Douglas**|0.23%|1.95%|0.20%
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+ ## Data Splits
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+ <hr style='margin-top:-1em; margin-bottom:0' />
73
  The polygons were sampled in southern France due to the partial availability of the Lidar data at the time of dataset creation.
74
  They are located in 40 distinct French administrative departments, covering a large diversity of territories and forests.
75
  To define a common benchmark, we divided the data into train, validation, and test sets, with a stratification on semantic labels.
 
84
  ![](./dataset_extent_map.excalidraw.png)
85
 
86
  ## Citation
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+ <hr style='margin-top:-1em; margin-bottom:0' />
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  Please include a citation to the following article if you use the PureForest dataset:
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  ```
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  @article{gaydon2024pureforest,
 
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  doi={TBD},
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  }
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  ```
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+
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  ## Dataset license
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  <hr style='margin-top:-1em; margin-bottom:0' />