metadata
configs:
- config_name: labeled_all
data_files: 50_all_traits.csv
default: true
description: Labeled spectra data with trait measurements for supervised learning.
- config_name: unlabeled
data_files: unlb/*.csv
description: Unlabeled spectra data for semi-supervised or self-supervised learning.
- config_name: labeled_splits
data_files:
- split: train
path:
- lb/train.csv
- split: test
path: lb/test.csv
🌱 GreenHySpectra: 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.
📁 Configurations
1. GreenHySpectra: Unlabeled Set
- Files: all CSVs under
unlb/ - 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:
50_all_traits.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/m²) |
| Cm | Leaf mass per area (g/m²) |
| 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) |