--- 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. ## šŸ“ 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 ---