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
π³ PrediTree: A Multi-Temporal Multi-Spectral Sub-Meter Canopy Height Maps Dataset
π Overview
PrediTree is a large-scale multi-temporal, multi-spectral canopy height dataset designed for π 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.
β¨ Key Highlights
- π Multi-Temporal: 3 yearly acquisitions (RGB + NIR + NDVI)
- π Multi-Spectral: High-resolution optical imagery including RGB, NIR, and derived NDVI
- π² Canopy Height Models (CHM): LiDAR-based data
- π Resolution: 0.5 m
- π Coverage: France-wide dataset with departmental splits
- π¦ Scale: 785k training patches, ~880 GB of data
π Dataset Structure
Each sample contains:
Column | Description |
---|---|
chm |
π² Canopy Height Model (m) |
rgbnir_ndvi_[1-3] |
πΈ RGB + NIR + NDVI imagery for three years (5 bands, 256Γ256) |
rgbnir_year_[1-3] |
π Acquisition year for imagery |
chm_mean_year |
ποΈ Average canopy height across years |
no_data_percentage |
β % missing pixels |
crs , transform , bounds , resolution |
πΊοΈ Geospatial metadata |
π 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
π¬ 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.
π 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}
}
π Tags
remote-sensing
Β· multi-temporal
Β· multi-spectral
Β· canopy-height-prediction
Β· infrared
Β· rgb
Β· model