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README.md
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# Dataset Card for PureForest
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## Context and Data
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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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## Dataset Structure
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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.
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:-------------------------:|:-------------------------:
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![](./imagery_18_classes.png) | ![](./lidar_18_classes.png)
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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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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.
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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,
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doi={TBD},
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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' />
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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' />
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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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## Dataset Structure
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<hr style='margin-top:-1em; margin-bottom:0' />
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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.
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:-------------------------:|:-------------------------:
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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
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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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| 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' />
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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.
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![](./dataset_extent_map.excalidraw.png)
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## 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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## Dataset license
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<hr style='margin-top:-1em; margin-bottom:0' />
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