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--- |
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configs: |
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- config_name: labeled_all |
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data_files: labeled_all/all.csv |
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default: true |
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description: Labeled spectra data with trait measurements for supervised learning. |
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- config_name: unlabeled |
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data_files: unlabeled/*.csv |
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description: Unlabeled spectra data for semi-supervised or self-supervised learning. |
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- config_name: labeled_splits |
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data_files: |
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- split: train |
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path: labeled_splits/train.csv |
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- split: test |
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path: labeled_splits/test.csv |
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description: A stratified splitting of the labeled data. |
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task_categories: |
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- image-feature-extraction |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- hyperspectral |
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- vegetation |
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- plant-traits |
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- remote-sensing |
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- climate-change |
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--- |
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# 🌱 GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction 🌱 |
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[Paper](https://huggingface.co/papers/2507.06806) |
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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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## 📁 Configurations |
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### 1. `GreenHyperSpectra: Unlabeled set` |
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- Files: all CSVs under `unlabeled/` |
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- Contains: |
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- Sample ID |
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- Spectral bands (400-2450 nm) >> 1721 bands |
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| Column | Description | |
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|----------------|-----------------------------------------| |
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| 400 | Reflectance at 400nm | |
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| ... | More spectral bands | |
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| 2450 | Reflectance at 2450nm | |
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--- |
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### 2. `Labeled set` |
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- File: `labeled/all.csv` |
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- Contains: |
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- Sample ID |
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- Dataset ID |
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- Spectral bands (400-2450 nm) >> 1721 bands |
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- Trait measurements (e.g., leaf chlorophyll, nitrogen content etc.) |
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| Column | Description | |
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|----------------|-----------------------------------------| |
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| dataset | Reference to the source of the dataset | |
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| 400 | Reflectance at 400nm | |
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| ... | More spectral bands | |
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| 2450 | Reflectance at 2450nm | |
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| Cp | Nitrogen content (g/cm²) | |
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| Cm | Leaf mass per area (g/cm²) | |
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| Cw | Leaf water content (cm) | |
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| LAI | Leaf area index (m²/m²) | |
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| Cab | Leaf chrolophyll content (µg/m²) | |
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| Car | Leaf carotenoids content (µg/m²) | |
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| Anth | Leaf anthocynins content (µg/m²) | |
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| Cbc | Carbon-based constituents (g/cm²) | |
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--- |
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### 3. `Split labeled set` |
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- Files: all CSVs under `labeled_splits/` |
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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. |
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> ⚠️ **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. |
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> |
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> To work effectively with this dataset, we recommend using the **Hugging Face `datasets` library** or the **MLCroissant Python library** for programmatic access and exploration. |
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``` |
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### Check the data with Hugging Face datasets library ### |
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from datasets import load_dataset |
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### labeled_all ### |
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# Login using e.g. `huggingface-cli login` to access this dataset |
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ds = load_dataset("Avatarr05/GreenHyperSpectra", "labeled_all") |
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df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1) |
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display(df.head()) |
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### labeled_splits: train ### |
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train_dataset = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train") |
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train_dataset = train_dataset.to_pandas().drop(['Unnamed: 0'], axis=1) |
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display(df.head()) |
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``` |
