🌳 PrediTree: A Multi-Temporal Multi-Spectral Sub-Meter Canopy Height Maps Dataset

📖 Overview

PrediTree is a large-scale multi-temporal, multi-spectral canopy height dataset designed for 🌍 remote sensing, forestry monitoring, and environmental analysis.
All imagery and canopy height products are spatially aligned at 0.5 m resolution, enabling fine-grained tree growth prediction and ecological studies.

---

✨ Key Highlights

• 📊 Multi-Temporal: 3 yearly acquisitions (RGB + NIR + NDVI)
• 🌈 Multi-Spectral: High-resolution optical imagery including RGB, NIR, and derived NDVI
• 🌲 Canopy Height Models (CHM): LiDAR-based data
• 📏 Resolution: 0.5 m
• 🌍 Coverage: France-wide dataset with departmental splits
• 📦 Scale: 785k training patches, ~880 GB of data

---

📂 Dataset Structure

Each sample contains:

Column	Description
chm	🌲 Canopy Height Model (m)
rgbnir_ndvi_[1-3]	📸 RGB + NIR + NDVI imagery for three years (5 bands, 256×256)
rgbnir_year_[1-3]	📅 Acquisition year for imagery
chm_mean_year	🏞️ Average canopy height across years
no_data_percentage	❌ % missing pixels
crs, transform, bounds, resolution	🗺️ Geospatial metadata

---

📊 Dataset Specs

splits:
  train:
    num_examples: 785,392
    256_256px_subtile_examples: 3,141,568
    size: 880 GB
resolution: 0.5 m
dataset_size: 880 GB
license: apache-2.0

---

🔬 Scientific Context

PrediTree is the first CHM dataset to offer multi-temporal sub-meter CHM-aligned imagery specifically designed for training and evaluating tree height prediction models.

---

📜 Citation

If you use this dataset, please cite:

@inproceedings{debary2025preditree,
  title={PrediTree: A Multi-Temporal Sub-meter Dataset of Multi-Spectral Imagery Aligned With Canopy Height Maps},
  author={Debary, Hiyam and Fiaz, Mustansar and Klein, Levente},
  booktitle={GAIA},
  year={2025},
  url={https://huggingface.co/datasets/hiyam-d/PrediTree}
}

---

🔖 Tags

remote-sensing · multi-temporal · multi-spectral · canopy-height-prediction · infrared · rgb · model