onboard-coop/emit-test-dataset

Dataset Card for EMIT Test Dataset

Dataset Description

This dataset contains a comprehensive test data sample derived from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument, an imaging spectrometer operating aboard the International Space Station (ISS).

This repository was created to accompany the article [Insert Article Title Here]. It provides multiple EMIT Level 1B Radiance granules alongside corresponding machine learning inference outputs, ground truth labels, and visualisations. It is designed to evaluate and demonstrate [Insert what your article demonstrates, e.g., onboard anomaly detection, plume segmentation using LinkNet, etc.].

• Associated Article: [Link to your paper/article]
• Original Data Source: NASA JPL EMIT Data Portal
• Total Size: ~108 GB

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Dataset Structure

The dataset consists of a main data/ directory containing multiple subdirectories. Each subdirectory represents a distinct EMIT overpass/granule (e.g., EMIT_L1B_RAD_001_20230203T171446_2303412_007, EMIT_L1B_RAD_001_20240120T154618_2402010_008, etc.).

Within each of these granule directories, you will find the following file structure:

Raw Hyperspectral Data

• EMIT_L1B_RAD_...nc (~3.1 GB): The original Level 1B Radiance NetCDF file containing the raw hyperspectral data cube from the EMIT instrument.

Visualisations

• 40_rgb.png: A standard RGB composite rendering of the scene for visual reference.
• Mag1c Renders: Visualisations of the Mag1c algorithm outputs at different maximum threshold values for easy inspection.
  • 40_mag1c_1750max.png
  • 40_mag1c_3500max.png
  • 40_mag1c_5250max.png
  • 40_mag1c_7000max.png

Machine Learning Labels & Inference

• label.npy: Ground truth labels (array format) for the scene.
• label.png: Visual representation of the ground truth labels.
• linknet_inference.npy: The stitched predictions from the LinkNet model.
• mag1c_sas.npy: Mag1c-SAS output.
• mask.npy: Binary array used to filter invalid regions of the image.

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Citation

If you use this dataset in your research, please cite the accompanying article:

@article{your_citation_key,
  title={[Insert Article Title]},
  author={[Insert Authors]},
  journal={[Insert Journal]},
  year={[Insert Year]},
  url={[Insert URL]}
}