README.md

metadata

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.
task_categories:
  - image-feature-extraction
license: cc-by-nc-4.0
language:
  - en
tags:
  - hyperspectral
  - vegetation
  - plant-traits
  - remote-sensing
  - climate-change

🌱 GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction 🌱

Paper

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.

📁 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

---

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)

---

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.

⚠️ 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.

### Check the data with Hugging Face datasets library ###

from datasets import load_dataset

### labeled_all ###
# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("Avatarr05/GreenHyperSpectra", "labeled_all")

df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
display(df.head())

### labeled_splits: train ###
train_dataset = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train")

train_dataset = train_dataset.to_pandas().drop(['Unnamed: 0'], axis=1)
display(df.head())