Duplicate from Avatarr05/GreenHyperSpectra

Co-authored-by: Avatarr05 <Avatarr05@users.noreply.huggingface.co>

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README.md

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+---
+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.
+license: cc-by-nc-4.0
+task_categories:
+- feature-extraction
+language:
+- en
+tags:
+- hyperspectral
+- plant-traits
+- remote-sensing
+- vegetation
+- multi-regression
+size_categories:
+- 100K<n<1M
+---
+# 🌱 GreenHyperSpectra: 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.
+
+<!-- ![DatasetIcon](./MultiSourceIcon.png) -->
+<p align="center">
+  <img src="./MultiSourceIcon.png" alt="DatasetIcon" width="40%"/>
+</p>
+
+# Spatial coverage
+<!-- ![Dataset overview](./map_HF.png) -->
+<p align="center">
+  <img src="./map_HF.png" alt="Dataset overview" width="60%">
+</p>
+
+## 📁 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                   |
+<!-- --- -->
+#### Check the data with Hugging Face datasets library
+```
+from datasets import load_dataset
+
+### GreenHyperSpectra: unlabeled ###
+ds_un = load_dataset("Avatarr05/GreenHyperSpectra", "unlabeled")
+GreenHyperSpectra = ds_un['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
+
+display(GreenHyperSpectra.head())
+```
+---
+
+### 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)       |
+<!-- --- -->
+#### Check the data with Hugging Face datasets library
+```
+from datasets import load_dataset
+
+### Labeled data: labeled_all ###
+ds = load_dataset("Avatarr05/GreenHyperSpectra", "labeled_all")
+df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
+
+display(df.head())
+```
+---
+### 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.
+
+#### Check the data with Hugging Face datasets library
+```
+from datasets import load_dataset
+
+### Labeled splits: labeled_splits ###
+annotated_ds_train = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train")
+annotated_ds_train = annotated_ds_train['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
+
+annotated_ds_test = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="test")
+annotated_ds_test = annotated_ds_test['train'].to_pandas().drop(['Unnamed: 0'], axis=1)
+
+display(annotated_ds_train.head())
+display(annotated_ds_test.head())
+```
+> ⚠️ **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.
+
+## Citation
+If you use the **GreenHyperSpectra** dataset, please cite the following paper:
+
+```bibtex
+@article{cherif2025greenhyperspectra,
+  title={GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction},
+  author={Cherif, Eya and Ouaknine, Arthur and Brown, Luke A and Dao, Phuong D and Kovach, Kyle R and Lu, Bing and Mederer, Daniel and Feilhauer, Hannes and Kattenborn, Teja and Rolnick, David},
+  journal={arXiv preprint arXiv:2507.06806},
+  year={2025}
+}
+```
+
+If you use the labeled data included in this repository, please also cite the following study for more details about the compiled datasets:
+
+```bibtex
+@article{cherif2023spectra,
+  title={From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data},
+  author={Cherif, Eya and Feilhauer, Hannes and Berger, Katja and Dao, Phuong D and Ewald, Michael and Hank, Tobias B and He, Yuhong and Kovach, Kyle R and Lu, Bing and Townsend, Philip A and others},
+  journal={Remote Sensing of Environment},
+  volume={292},
+  pages={113580},
+  year={2023},
+  publisher={Elsevier}
+}
+```
+license: cc-by-nc-4.0
+---

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