waveyellow/GreenHyperSpectra

🌱 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.

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Spatial coverage

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📁 Configurations

1. GreenHyperSpectra: Unlabeled set

• —Files: all CSVs under unlabeled/
• —Contains:
• —Sample ID
• —Spectral bands (400-2450 nm) >> 1721 bands

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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.)

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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:

@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:

@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 ---