Datasets:
File size: 5,973 Bytes
eafed96 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | ---
configs:
- config_name: labeled_all
data_files: labeled_all/all.csv
default: true
description: Labeled spectra data with trait measurements for supervised learning.
- config_name: unlabeled
data_files: unlabeled/*.csv
description: Unlabeled spectra data for semi-supervised or self-supervised learning.
- config_name: labeled_splits
data_files:
- split: train
path: labeled_splits/train.csv
- split: test
path: labeled_splits/test.csv
description: A stratified splitting of the labeled data.
license: cc-by-nc-4.0
task_categories:
- feature-extraction
language:
- en
tags:
- hyperspectral
- plant-traits
- remote-sensing
- vegetation
- multi-regression
size_categories:
- 100K<n<1M
---
# 🌱 GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction 🌱
GreenHySpectra is a collection of hyperspectral reflectance data of vegetation from different sources. It is intended for Regression machine learning task for plant trait prediction with self and semi-supervised learning.
<!--  -->
<p align="center">
<img src="./MultiSourceIcon.png" alt="DatasetIcon" width="40%"/>
</p>
# Spatial coverage
<!--  -->
<p align="center">
<img src="./map_HF.png" alt="Dataset overview" width="60%">
</p>
## 📁 Configurations
### 1. `GreenHyperSpectra: Unlabeled set`
- Files: all CSVs under `unlabeled/`
- Contains:
- Sample ID
- Spectral bands (400-2450 nm) >> 1721 bands
| Column | Description |
|----------------|-----------------------------------------|
| 400 | Reflectance at 400nm |
| ... | More spectral bands |
| 2450 | Reflectance at 2450nm |
<!-- --- -->
#### Check the data with Hugging Face datasets library
```
from datasets import load_dataset
### GreenHyperSpectra: unlabeled ###
ds_un = load_dataset("Avatarr05/GreenHyperSpectra", "unlabeled")
GreenHyperSpectra = ds_un['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
display(GreenHyperSpectra.head())
```
---
### 2. `Labeled set`
- File: `labeled/all.csv`
- Contains:
- Sample ID
- Dataset ID
- Spectral bands (400-2450 nm) >> 1721 bands
- Trait measurements (e.g., leaf chlorophyll, nitrogen content etc.)
| Column | Description |
|----------------|-----------------------------------------|
| dataset | Reference to the source of the dataset |
| 400 | Reflectance at 400nm |
| ... | More spectral bands |
| 2450 | Reflectance at 2450nm |
| Cp | Nitrogen content (g/cm²) |
| Cm | Leaf mass per area (g/cm²) |
| Cw | Leaf water content (cm) |
| LAI | Leaf area index (m²/m²) |
| Cab | Leaf chrolophyll content (µg/m²) |
| Car | Leaf carotenoids content (µg/m²) |
| Anth | Leaf anthocynins content (µg/m²) |
| Cbc | Carbon-based constituents (g/cm²) |
<!-- --- -->
#### Check the data with Hugging Face datasets library
```
from datasets import load_dataset
### Labeled data: labeled_all ###
ds = load_dataset("Avatarr05/GreenHyperSpectra", "labeled_all")
df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
display(df.head())
```
---
### 3. `Split labeled set`
- Files: all CSVs under `labeled_splits/`
These files follow the same format as the previous set but are pre-split for machine learning purposes. The split is stratified based on the dataset ID, with 20% of the data reserved for testing.
#### Check the data with Hugging Face datasets library
```
from datasets import load_dataset
### Labeled splits: labeled_splits ###
annotated_ds_train = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train")
annotated_ds_train = annotated_ds_train['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
annotated_ds_test = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="test")
annotated_ds_test = annotated_ds_test['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
display(annotated_ds_train.head())
display(annotated_ds_test.head())
```
> ⚠️ **Note:** Due to the high dimensionality of spectral datasets—often containing hundreds or thousands of columns—**Hugging Face Data Studio may not render these files properly**. This is a known limitation, as the Studio interface is not optimized for wide tabular data.
>
> To work effectively with this dataset, we recommend using the **Hugging Face `datasets` library** or the **MLCroissant Python library** for programmatic access and exploration.
## Citation
If you use the **GreenHyperSpectra** dataset, please cite the following paper:
```bibtex
@article{cherif2025greenhyperspectra,
title={GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction},
author={Cherif, Eya and Ouaknine, Arthur and Brown, Luke A and Dao, Phuong D and Kovach, Kyle R and Lu, Bing and Mederer, Daniel and Feilhauer, Hannes and Kattenborn, Teja and Rolnick, David},
journal={arXiv preprint arXiv:2507.06806},
year={2025}
}
```
If you use the labeled data included in this repository, please also cite the following study for more details about the compiled datasets:
```bibtex
@article{cherif2023spectra,
title={From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data},
author={Cherif, Eya and Feilhauer, Hannes and Berger, Katja and Dao, Phuong D and Ewald, Michael and Hank, Tobias B and He, Yuhong and Kovach, Kyle R and Lu, Bing and Townsend, Philip A and others},
journal={Remote Sensing of Environment},
volume={292},
pages={113580},
year={2023},
publisher={Elsevier}
}
```
license: cc-by-nc-4.0
--- |