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---
configs:
- config_name: labeled_all
data_files: "50_all_traits.csv"
# data_files:
# - split: all
# path:
# - "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"
# description: "A stratified splitting of the labeled data."
---
# 🌱 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) |
---
license: mit
---
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