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license: cc-by-4.0 |
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pipeline_tag: image-segmentation |
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# Model Card |
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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."** |
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**Paper:** [Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware](https://huggingface.co/papers/2507.01472) |
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## Model Overview |
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The models here were trained using the code available at the following GitHub repository: |
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* **Training Code**: [HyperspectralViTs](https://github.com/previtus/HyperspectralViTs) |
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The main project code, including filters benchmarking and demos, is available at the: |
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* **Project Code**: [Methane Filters Benchmark](https://github.com/zaitra/methane-filters-benchmark) |
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## Data and Products |
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The precomputed products used for training were created by code in the main project repository: |
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* **Products Creation Code**: [methane-filters-benchmark](https://github.com/zaitra/methane-filters-benchmark) |
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Additionally, these precomputed products are hosted and accessible here: |
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* **Dataset Repository**: [STARCOP-fast-products](https://huggingface.co/datasets/onboard-coop/STARCOP-fast-products) |
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## Sample Usage |
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You can try out our models and demos directly in Google Colab using the provided notebooks: |
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* **Models Demo**: [](https://colab.research.google.com/github/zaitra/methane-filters-benchmark/blob/main/ntbs/Models_demo.ipynb) |
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This notebook demonstrates model inference. |
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* **Products Creation and Benchmarking Demo**: [](https://colab.research.google.com/github/zaitra/methane-filters-benchmark/blob/main/ntbs/Products_demo.ipynb) |
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This notebook demonstrates generating products and measuring their runtime. |
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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). |
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## Citation |
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If you use these models in your research, please cite our article: |
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```bibtex |
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@misc{herec2025optimizingmethanedetectionboard, |
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title={Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware}, |
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author={Jonáš Herec and Vít Růžička and Rado Pitoňák}, |
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year={2025}, |
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eprint={2507.01472}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2507.01472}, |
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} |
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``` |
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## Models performance |
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## U-Net - CEM |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.441 | 0.317 | 0.369 | 0.500 | |
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| B | 0.701 | 0.158 | 0.258 | 0.550 | |
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| C | 0.531 | 0.299 | 0.382 | 0.610 | |
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| D | 0.536 | 0.218 | 0.310 | 0.551 | |
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| E | 0.564 | 0.182 | 0.275 | 0.469 | |
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| **AVG** | 55.47% | 23.49% | 31.90% | 53.55% | |
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| **STD** | 8.41% | 6.30% | 4.94% | 4.85% | |
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--- |
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## U-Net - ACE |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.468 | 0.202 | 0.282 | 0.460 | |
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| B | 0.480 | 0.288 | 0.360 | 0.537 | |
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| C | 0.413 | 0.253 | 0.314 | 0.461 | |
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| D | 0.550 | 0.194 | 0.287 | 0.510 | |
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| E | 0.500 | 0.162 | 0.245 | 0.442 | |
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| **AVG** | 48.22% | 21.99% | 29.77% | 48.19% | |
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| **STD** | 4.46% | 4.50% | 3.82% | 3.57% | |
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--- |
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## U-Net - MF |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.603 | 0.153 | 0.243 | 0.451 | |
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| B | 0.673 | 0.198 | 0.306 | 0.585 | |
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| C | 0.563 | 0.259 | 0.355 | 0.507 | |
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| D | 0.625 | 0.173 | 0.271 | 0.558 | |
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| E | 0.466 | 0.301 | 0.366 | 0.496 | |
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| **AVG** | 58.60% | 21.68% | 30.82% | 51.94% | |
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| **STD** | 6.97% | 5.52% | 4.73% | 4.73% | |
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--- |
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## U-Net - MAG1C-SAS |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.587 | 0.456 | 0.513 | 0.668 | |
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| B | 0.618 | 0.291 | 0.395 | 0.642 | |
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| C | 0.576 | 0.290 | 0.386 | 0.604 | |
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| D | 0.613 | 0.414 | 0.495 | 0.686 | |
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| E | 0.427 | 0.280 | 0.338 | 0.470 | |
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| **AVG** | 56.42% | 34.62% | 42.54% | 61.40% | |
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| **STD** | 7.04% | 7.38% | 6.73% | 7.71% | |
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## U-Net - MAG1C (tile-wise) |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.643 | 0.218 | 0.325 | 0.599 | |
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| B | 0.732 | 0.288 | 0.413 | 0.692 | |
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| C | 0.613 | 0.362 | 0.455 | 0.659 | |
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| D | 0.669 | 0.242 | 0.355 | 0.633 | |
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| E | 0.640 | 0.366 | 0.466 | 0.684 | |
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| **AVG** | 65.94% | 29.52% | 40.28% | 65.34% | |
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| **STD** | 4.04% | 6.05% | 5.51% | 3.42% | |
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--- |
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## LinkNet - CEM |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.597 | 0.319 | 0.416 | 0.633 | |
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| B | 0.539 | 0.274 | 0.363 | 0.603 | |
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| C | 0.452 | 0.233 | 0.308 | 0.527 | |
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| D | 0.606 | 0.165 | 0.260 | 0.561 | |
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| E | 0.442 | 0.144 | 0.217 | 0.455 | |
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| **AVG** | 52.72% | 22.70% | 31.27% | 55.56% | |
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| **STD** | 6.96% | 6.54% | 7.09% | 6.20% | |
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--- |
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## LinkNet - MAG1C-SAS |
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| ID | Recall | Precision | F1 | F1 strong | |
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|----|--------|-----------|-------|-----------| |
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| A | 0.566 | 0.324 | 0.412 | 0.612 | |
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| B | 0.505 | 0.515 | 0.510 | 0.613 | |
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| C | 0.381 | 0.422 | 0.400 | 0.507 | |
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| D | 0.590 | 0.383 | 0.464 | 0.660 | |
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| E | 0.513 | 0.378 | 0.435 | 0.627 | |
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| **AVG** | 51.10% | 40.44% | 44.42% | 60.38% | |
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| **STD** | 7.24% | 6.35% | 3.95% | 5.14% | |
