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---
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
[](https://huggingface.co/datasets/hiyam-d/vhr_canopy_height_allier_50cm_small)
[](https://arxiv.org/)
[](https://www.apache.org/licenses/LICENSE-2.0)
## 📖 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
```yaml
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:
```bibtex
@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` |