PrediTree / README.md
hiyam-d's picture
fix: title+parameters
f397931 verified
metadata
dataset_info:
  features:
    - name: id
      dtype: string
    - name: department_name
      dtype: string
    - name: chm
      dtype:
        array2_d:
          shape:
            - 256
            - 256
          dtype: float16
    - name: no_data_percentage
      dtype: float32
    - name: crs
      dtype: string
    - name: transform
      dtype: string
    - name: bounds
      dtype: string
    - name: resolution
      dtype: float32
    - name: chm_mean_year
      dtype: int16
    - name: rgbnir_ndvi_1
      dtype:
        array3_d:
          shape:
            - 5
            - 256
            - 256
          dtype: uint8
    - name: rgbnir_year_1
      dtype: uint16
    - name: rgbnir_ndvi_2
      dtype:
        array3_d:
          shape:
            - 5
            - 256
            - 256
          dtype: uint8
    - name: rgbnir_year_2
      dtype: uint16
    - name: rgbnir_ndvi_3
      dtype:
        array3_d:
          shape:
            - 5
            - 256
            - 256
          dtype: uint8
    - name: rgbnir_year_3
      dtype: uint16
  splits:
    - name: train
      num_bytes: 880058054404
      num_examples: 785392
  download_size: 730412322573
  dataset_size: 880058054404
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: apache-2.0
pretty_name: PrediTree
tags:
  - remote-sensing
  - multi-temporal
  - multi-spectral
  - canopy-height-prediction
  - 3-pg
  - infrared
  - rgb
  - model

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

Dataset Paper License

Sample Panels

πŸ“– 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

  • πŸ“Š 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
  • πŸ“ Resolution: 0.5 m
  • 🌍 Coverage: France-wide dataset with departmental splits
  • πŸ“¦ Scale: 785k training patches, ~880 GB of data

πŸ“‚ Dataset Structure

Each sample contains:

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

πŸ“Š 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

πŸ”¬ 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.

Comparison with Existing Datasets


πŸ“œ 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}
}

πŸ”– Tags

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