--- license: etalab-2.0 task_categories: - image-classification - other tags: - Aerial - Lidar - Point Cloud - Forest - Tree Species - Earth Observation - Multimodal - IGN size_categories: - 100K **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).