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README.md

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+---
+license: etalab-2.0
+task_categories:
+  - image-classification
+  - other
+tags:
+  - Aerial
+  - Lidar
+  - Point Cloud
+  - Forest
+  - Tree Species
+  - Earth Observation
+  - Multimodal
+  - IGN
+size_categories:
+  - 100K<n<1M
+---
+
+# PureForest Clone
+
+This is a clone of the original [IGNF/PureForest](https://huggingface.co/datasets/IGNF/PureForest) dataset,
+re-uploaded as zip archives for easier downloading and use.
+
+> **Original paper:** [PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests](https://arxiv.org/abs/2404.12064)
+> Charles Gaydon, Floryne Roche — IGN, 2024
+
+---
+
+## Dataset Overview
+
+PureForest is the largest publicly available dataset for tree species classification from:
+- **ALS (Aerial Lidar Scanning)** point clouds — high density: ~40 pts/m²
+- **VHR (Very High Resolution)** aerial images — 0.2 m resolution, 250×250 pixels, NIRGB channels
+
+| Property | Value |
+|----------|-------|
+| Total patches | 135,569 |
+| Coverage | 339 km² |
+| Forests | 449 distinct monospecific forests |
+| Departments | 40 French departments |
+| Semantic classes | 13 (grouping 18 tree species) |
+| Patch size | 50 m × 50 m |
+
+---
+
+## Dataset Structure
+
+```
+PureForest_clone/
+├── train.zip        # 69,111 patches (~51.4 GB)
+├── val.zip          # 13,523 patches (~10 GB)
+├── test.zip         # 52,935 patches (~39.3 GB)
+└── metadata/        # Metadata files
+```
+
+Each `.npz` patch file contains:
+- `intensities` — Lidar intensity values
+- `classes` — semantic class label (integer 0–12)
+- Point cloud coordinates (x, y, z) colorized with aerial imagery
+
+### File Naming Convention
+
+```
+{SPLIT}-{ClassName}-C{ClassID}-{PatchID}.npz
+
+Example: TRAIN-Quercus_ilex-C1-177_13_242.npz
+         TEST-Abies_alba-C9-001_02_015.npz
+```
+
+### Semantic Classes
+
+| Class ID | Species | Common Name |
+|----------|---------|-------------|
+| 0 | Quercus (deciduous) | Deciduous oak |
+| 1 | Quercus ilex | Evergreen oak |
+| 2 | Fagus sylvatica | Beech |
+| 3 | Castanea sativa | Chestnut |
+| 4 | Robinia pseudoacacia | Black locust |
+| 5 | Pinus pinaster | Maritime pine |
+| 6 | Pinus sylvestris | Scotch pine |
+| 7 | Pinus nigra | Black pine |
+| 8 | Pinus halepensis | Aleppo pine |
+| 9 | Abies alba | Fir |
+| 10 | Picea abies | Spruce |
+| 11 | Larix decidua | Larch |
+| 12 | Pseudotsuga menziesii | Douglas |
+
+### Train/Val/Test Split Distribution
+
+| Class | Train | Val | Test |
+|-------|-------|-----|------|
+| (0) Deciduous oak | 22.92% | 32.35% | 52.59% |
+| (1) Evergreen oak | 16.80% | 2.75% | 19.61% |
+| (2) Beech | 10.14% | 12.03% | 7.62% |
+| (3) Chestnut | 4.83% | 1.09% | 0.38% |
+| (4) Black locust | 2.41% | 2.40% | 0.60% |
+| (5) Maritime pine | 6.61% | 7.10% | 3.85% |
+| (6) Scotch pine | 16.39% | 17.95% | 8.51% |
+| (7) Black pine | 6.30% | 6.98% | 3.64% |
+| (8) Aleppo pine | 5.83% | 1.72% | 0.83% |
+| (9) Fir | 0.14% | 5.32% | 0.05% |
+| (10) Spruce | 3.73% | 4.64% | 1.64% |
+| (11) Larch | 3.67% | 3.73% | 0.48% |
+| (12) Douglas | 0.23% | 1.95% | 0.20% |
+
+---
+
+## Download & Setup
+
+### Step 1 — Download zip files
+
+```python
+from huggingface_hub import hf_hub_download
+
+for split in ["train", "val", "test"]:
+    hf_hub_download(
+        repo_id="longdpkr/PureForest_clone",
+        filename=f"{split}.zip",
+        repo_type="dataset",
+        local_dir="./PureForest"
+    )
+```
+
+Or using the CLI:
+```bash
+huggingface-cli download longdpkr/PureForest_clone train.zip --repo-type=dataset
+huggingface-cli download longdpkr/PureForest_clone val.zip --repo-type=dataset
+huggingface-cli download longdpkr/PureForest_clone test.zip --repo-type=dataset
+```
+
+### Step 2 — Extract zip files
+
+```python
+import zipfile
+from pathlib import Path
+
+dataset_dir = Path("./PureForest")
+
+for split in ["train", "val", "test"]:
+    zip_path = dataset_dir / f"{split}.zip"
+    print(f"Extracting {split}.zip ...")
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        zf.extractall(dataset_dir)
+    print(f"Done: {split}/")
+```
+
+Or manually using WinRAR / 7-Zip.
+
+After extraction, the structure should be:
+```
+PureForest/
+├── train/
+│   ├── TRAIN-Quercus_ilex-C1-xxx.npz
+│   └── ...
+├── val/
+│   ├── VAL-Fagus_sylvatica-C2-xxx.npz
+│   └── ...
+└── test/
+    ├── TEST-Pinus_sylvestris-C6-xxx.npz
+    └── ...
+```
+
+---
+
+## Loading Data
+
+### Load a single patch
+
+```python
+import numpy as np
+
+data = np.load("train/TRAIN-Quercus_ilex-C1-177_13_242.npz")
+
+print(list(data.keys()))     # show available keys
+print(data["classes"])       # class label (0-12)
+```
+
+### PyTorch Dataset
+
+```python
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from pathlib import Path
+
+
+class PureForestDataset(Dataset):
+    def __init__(self, split="train", data_dir="./PureForest"):
+        self.files = sorted(Path(data_dir) / split).glob("*.npz"))
+        
+    def __len__(self):
+        return len(self.files)
+    
+    def __getitem__(self, idx):
+        data = np.load(self.files[idx])
+        label = int(data["classes"])
+        # add your preprocessing here
+        return data, label
+
+
+train_dataset = PureForestDataset(split="train")
+val_dataset   = PureForestDataset(split="val")
+test_dataset  = PureForestDataset(split="test")
+
+print(f"Train: {len(train_dataset)} | Val: {len(val_dataset)} | Test: {len(test_dataset)}")
+```
+
+### With RandLA-Net
+
+This dataset is compatible with [RandLA-Net](https://github.com/QingyongHu/RandLA-Net) for point cloud classification.
+Place extracted folders under the `datasets/` directory of your RandLA-Net project:
+
+```
+RandLA-Net/
+└── datasets/
+    └── PureForest/
+        ├── train/
+        ├── val/
+        └── test/
+```
+
+---
+
+## License
+
+This dataset is a clone of [IGNF/PureForest](https://huggingface.co/datasets/IGNF/PureForest)
+and inherits its original license: **[Etalab Open License 2.0](https://www.etalab.gouv.fr/licence-ouverte-open-licence/)**.
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite the original paper:
+
+```bibtex
+@misc{gaydon2024pureforest,
+    title     = {PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset
+                 for Tree Species Classification in Monospecific Forests},
+    author    = {Charles Gaydon and Floryne Roche},
+    year      = {2024},
+    eprint    = {2404.12064},
+    archivePrefix = {arXiv},
+    url       = {https://arxiv.org/abs/2404.12064}
+}
+```
+
+---
+
+## Acknowledgements
+
+Original dataset by [IGN — Institut national de l'information géographique et forestière](https://www.ign.fr/).
+Lidar data from the [Lidar HD program](https://geoservices.ign.fr/lidarhd).
+Aerial imagery from [ORTHO HR®](https://geoservices.ign.fr/bdortho).