---
license: cc-by-4.0
pipeline_tag: image-segmentation
---

# 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](https://huggingface.co/papers/2507.01472)

## Model Overview

The models here were trained using the code available at the following GitHub repository:
*   **Training Code**: [HyperspectralViTs](https://github.com/previtus/HyperspectralViTs)

The main project code, including filters benchmarking and demos, is available at the:
*   **Project Code**: [Methane Filters Benchmark](https://github.com/zaitra/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](https://github.com/zaitra/methane-filters-benchmark)

Additionally, these precomputed products are hosted and accessible here:
*   **Dataset Repository**: [STARCOP-fast-products](https://huggingface.co/datasets/onboard-coop/STARCOP-fast-products)

## Sample Usage

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

*   **Models Demo**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zaitra/methane-filters-benchmark/blob/main/ntbs/Models_demo.ipynb)
    This notebook demonstrates model inference.

*   **Products Creation and Benchmarking Demo**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zaitra/methane-filters-benchmark/blob/main/ntbs/Products_demo.ipynb)
    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](https://github.com/zaitra/methane-filters-benchmark).

## Citation

If you use these models in your research, please cite our article:
```bibtex
@misc{herec2025optimizingmethanedetectionboard,
      title={Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware},
      author={Jonáš Herec and Vít Růžička and Rado Pitoňák},
      year={2025},
      eprint={2507.01472},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.01472},
}
```

## Models performance
## U-Net - CEM

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.441  | 0.317     | 0.369 | 0.500     |
| B  | 0.701  | 0.158     | 0.258 | 0.550     |
| C  | 0.531  | 0.299     | 0.382 | 0.610     |
| D  | 0.536  | 0.218     | 0.310 | 0.551     |
| E  | 0.564  | 0.182     | 0.275 | 0.469     |
| **AVG** | 55.47% | 23.49% | 31.90% | 53.55% |
| **STD** | 8.41% | 6.30% | 4.94% | 4.85% |

---

## U-Net - ACE

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.468  | 0.202     | 0.282 | 0.460     |
| B  | 0.480  | 0.288     | 0.360 | 0.537     |
| C  | 0.413  | 0.253     | 0.314 | 0.461     |
| D  | 0.550  | 0.194     | 0.287 | 0.510     |
| E  | 0.500  | 0.162     | 0.245 | 0.442     |
| **AVG** | 48.22% | 21.99% | 29.77% | 48.19% |
| **STD** | 4.46% | 4.50% | 3.82% | 3.57% |

---

## U-Net - MF

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.603  | 0.153     | 0.243 | 0.451     |
| B  | 0.673  | 0.198     | 0.306 | 0.585     |
| C  | 0.563  | 0.259     | 0.355 | 0.507     |
| D  | 0.625  | 0.173     | 0.271 | 0.558     |
| E  | 0.466  | 0.301     | 0.366 | 0.496     |
| **AVG** | 58.60% | 21.68% | 30.82% | 51.94% |
| **STD** | 6.97% | 5.52% | 4.73% | 4.73% |

---

## U-Net - MAG1C-SAS

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.587  | 0.456     | 0.513 | 0.668     |
| B  | 0.618  | 0.291     | 0.395 | 0.642     |
| C  | 0.576  | 0.290     | 0.386 | 0.604     |
| D  | 0.613  | 0.414     | 0.495 | 0.686     |
| E  | 0.427  | 0.280     | 0.338 | 0.470     |
| **AVG** | 56.42% | 34.62% | 42.54% | 61.40% |
| **STD** | 7.04% | 7.38% | 6.73% | 7.71% |

---

## U-Net - MAG1C (tile-wise)

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.643  | 0.218     | 0.325 | 0.599     |
| B  | 0.732  | 0.288     | 0.413 | 0.692     |
| C  | 0.613  | 0.362     | 0.455 | 0.659     |
| D  | 0.669  | 0.242     | 0.355 | 0.633     |
| E  | 0.640  | 0.366     | 0.466 | 0.684     |
| **AVG** | 65.94% | 29.52% | 40.28% | 65.34% |
| **STD** | 4.04% | 6.05% | 5.51% | 3.42% |

---

## LinkNet - CEM

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.597  | 0.319     | 0.416 | 0.633     |
| B  | 0.539  | 0.274     | 0.363 | 0.603     |
| C  | 0.452  | 0.233     | 0.308 | 0.527     |
| D  | 0.606  | 0.165     | 0.260 | 0.561     |
| E  | 0.442  | 0.144     | 0.217 | 0.455     |
| **AVG** | 52.72% | 22.70% | 31.27% | 55.56% |
| **STD** | 6.96% | 6.54% | 7.09% | 6.20% |

---

## LinkNet - MAG1C-SAS

| ID | Recall | Precision | F1    | F1 strong |
|----|--------|-----------|-------|-----------|
| A  | 0.566  | 0.324     | 0.412 | 0.612     |
| B  | 0.505  | 0.515     | 0.510 | 0.613     |
| C  | 0.381  | 0.422     | 0.400 | 0.507     |
| D  | 0.590  | 0.383     | 0.464 | 0.660     |
| E  | 0.513  | 0.378     | 0.435 | 0.627     |
| **AVG** | 51.10% | 40.44% | 44.42% | 60.38% |
| **STD** | 7.24% | 6.35% | 3.95% | 5.14% |