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---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- remote sensing
- EMIT
- Hyperspectral
- AVIRIS
- methane
- CH4
---
# STARCOP pre-trained models
This repository contains the trained models of the publication:
> V. Růžička, G. Mateo-Garcia, L. Gómez-Chova, A. Vaughan, L. Guanter, and A. Markham. [Semantic segmentation of methane plumes with hyperspectral machine learning models](https://www.nature.com/articles/s41598-023-44918-6). _Scientific Reports 13, 19999_ (2023). DOI: 10.1038/s41598-023-44918-6.
We include the trained models:
* **HyperSTARCOP, only mag1c** in folder `models/hyperstarcop_mag1c_only`
* **HyperSTARCOP, mag1c + rgb** in folder `models/hyperstarcop_mag1c_rgb`
The following table shows the performance of the models in the AVIRIS test dataset and in the EMIT test dataset:
In order to run any of these models:
* In a EMIT scene see the tutorial [*Run STARCOP models on raw EMIT data*](https://github.com/spaceml-org/STARCOP/blob/main/notebooks/emit_processing.ipynb).
If you find this work useful please cite:
```
@article{ruzicka_starcop_2023,
title = {Semantic segmentation of methane plumes with hyperspectral machine learning models},
volume = {13},
issn = {2045-2322},
url = {https://www.nature.com/articles/s41598-023-44918-6},
doi = {10.1038/s41598-023-44918-6},
number = {1},
journal = {Scientific Reports},
author = {Růžička, Vít and Mateo-Garcia, Gonzalo and Gómez-Chova, Luis and Vaughan, Anna, and Guanter, Luis and Markham, Andrew},
month = nov,
year = {2023},
pages = {19999}
}
``` |