fast-methane-filters-models

Model Card

This Hugging Face repository contains models trained in the article "Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware."

Paper: Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware

Model Overview

The models here were trained using the code available at the following GitHub repository:

• Training Code: HyperspectralViTs

The main project code, including filters benchmarking and demos, is available at the:

• Project Code: Methane Filters Benchmark

Data and Products

The precomputed products used for training were created by code in the main project repository:

• Products Creation Code: methane-filters-benchmark

Additionally, these precomputed products are hosted and accessible here:

• Dataset Repository: STARCOP-fast-products

Sample Usage

You can try out our models and demos directly in Google Colab using the provided notebooks:

• Models Demo: This notebook demonstrates model inference.

• Products Creation and Benchmarking Demo: This notebook demonstrates generating products and measuring their runtime.

For local inference using the ONNX models, refer to the benchmark/onnx_inference_time.py script in the Project Code repository.

Citation

If you use these models in your research, please cite our article:

@misc{herec2026fastmethanedetectionpipeline,
      title={A Fast Methane Detection Pipeline on Board Satellites Based on Mag1c-SAS and LinkNet}, 
      author={Jonáš Herec and Vít Růžička and Rado Pitoňák and Jan Sedmidubsky},
      year={2026},
      eprint={2606.03675},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.03675}, 
}

Models performance

U-Net - CEM

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U-Net - ACE

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U-Net - MF

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U-Net - MAG1C-SAS

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U-Net - MAG1C (tile-wise)

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LinkNet - CEM

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LinkNet - MAG1C-SAS

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U-Net - ACE_multiplicative

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U-Net - MF_multiplicative