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STARCOP

Semantic Segmentation of Methane Plumes with Hyperspectral ML Models

Dataset

The full annotated dataset used for training and evaluation is <a href="https://doi.org/10.5281/zenodo.7863343">hosted on Zenodo</a>. For easier access to the data for the demos, a smaller subsets are also hosted on Google Drive: <a href="https://drive.google.com/uc?id=1TwtSVpbvGd-lWfIjQrw0i4LqkiX2EuHq">evaluation dataset</a> and <a href="https://drive.google.com/uc?id=1C4ZHvT1ZPKVMFGmqcV12Aozs8Uv_DIxD">subset of the training dataset, including only strong plumes</a>. We provide selected AVIRIS-NG hyperspectral bands, computed methane enhancement products and simulated multispectral views of the data from WorldView-3. For more details see the paper.

All bands: If you'd like to use more AVIRIS-NG bands, please contact us for instructions on downloading the full data (a preview of the formatting in a mini dataset is also available <a href="https://huggingface.co/datasets/previtus/starcop_allbands_mini">here</a>).

For dataset inspection use the prepared <a href="https://colab.research.google.com/github/spaceml-org/STARCOP/blob/master/notebooks/dataset_exploration.ipynb">Colab Dataset Exploration demo <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a>.

Code examples

Install

conda create -c conda-forge -n starcop_env python=3.10 mamba
conda activate starcop_env

pip install git+https://github.com/spaceml-org/STARCOP.git

Inference

To start using our model for inference, you can check the demo with AVIRIS data in <a href="https://colab.research.google.com/github/spaceml-org/STARCOP/blob/master/notebooks/model_demos_AVIRIS.ipynb"> Colab Inference on AVIRIS <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a>, or with EMIT data in <a href="https://colab.research.google.com/github/spaceml-org/STARCOP/blob/master/notebooks/inference_on_raw_EMIT_nc_file.ipynb"> Colab Inference on EMIT <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a>. These download our annotated datasets and demonstrate the performance of our pre-trained models.

Our trained models are stored in Hugging Face🤗 at isp-uv-es/starcop.

Training

To reproduce the same training process as reported in the paper, you will need to download the whole STARCOP dataset from Zenodo first, and prepare the coding environment.

# Check possible parameters with:
!python3 -m scripts.train --help

# Or run the prepared training script used for the paper models (remember to download and adjust the paths to the training datasets)
./bash/bash_train_example.sh

Minimal training example

If you install the environment using the commands above, this should work as a minimal training example (which includes first getting the data):

gdown https://drive.google.com/uc?id=1Qw96Drmk2jzBYSED0YPEUyuc2DnBechl -O STARCOP_mini.zip
unzip -q STARCOP_mini.zip
# The train script will expect the test dataset in the "test.csv" - so here in this small demo we just place the small subset there instead:
cp STARCOP_mini/test_mini10.csv STARCOP_mini/test.csv

python -m scripts.train dataset.input_products=["mag1c","TOA_AVIRIS_640nm","TOA_AVIRIS_550nm","TOA_AVIRIS_460nm"] model.model_type='unet_semseg' model.pos_weight=1 experiment_name="HyperSTARCOP_magic_rgb_DEMO" dataloader.num_workers=4 dataset.use_weight_loss=True training.val_check_interval=0.5 training.max_epochs=5 products_plot=["rgb_aviris","mag1c","label","pred","differences"] dataset.weight_sampling=True dataset.train_csv="train_mini10.csv" dataset.root_folder=PATH_TO/STARCOP_mini wandb.wandb_entity="YOUR_ENTITY" wandb.wandb_project="starcop_project"

Citation

If you find the STARCOP models or dataset useful in your research, please consider citing our work.

@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 G{\'o}mez-Chova, Luis and Vaughan, Anna and Guanter, Luis and Markham, Andrew},
	month = nov,
	year = {2023},
	pages = {19999}
}

Acknowledgments

This research has been funded by ESA Cognitive Cloud Computing in Space initiative project number STARCOP I-2022-00380. It has been supported by the DEEPCLOUD project (PID2019-109026RB-I00) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Union (NextGenerationEU).