| --- |
| 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 |
| --- |
