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Article DOI:10.1038/s41598-023-47595-7 PyPI PyPI - Python Version PyPI - License DOI docs

awesome ml4floods

ML4Floods is an end-to-end ML pipeline for flood extent estimation: from data preprocessing, model training, model deployment to visualization.

awesome flood extent estimation

Install

Install from pip:

pip install ml4floods

Install the latest version from GitHub:

pip install git+https://github.com/spaceml-org/ml4floods#egg=ml4floods

Docs

docs

These tutorials may help you explore the datasets and models:

The WorldFloods database

DOI

The WorldFloods database contains 509 pairs of Sentinel-2 images and flood segmentation masks. It requires approximately 300GB of hard-disk storage.

The WorldFloods database and all pre-trained models are released under a Creative Commons non-commercial licence licence

To download the WorldFloods database or the pretrained flood segmentation models see the instructions to download the database.

Cite

If you find this work useful please cite:

@article{portales-julia_global_2023,
	title = {Global flood extent segmentation in optical satellite images},
	volume = {13},
	issn = {2045-2322},
	doi = {10.1038/s41598-023-47595-7},
	number = {1},
	urldate = {2023-11-30},
	journal = {Scientific Reports},
	author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},
	month = nov,
	year = {2023},
	pages = {20316},
}
@article{mateo-garcia_towards_2021,
	title = {Towards global flood mapping onboard low cost satellites with machine learning},
	volume = {11},
	issn = {2045-2322},
	doi = {10.1038/s41598-021-86650-z},
	number = {1},
	urldate = {2021-04-01},
	journal = {Scientific Reports},
	author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Joshua and Smith, Lewis and Oprea, Silviu Vlad and Schumann, Guy and Gal, Yarin and Baydin, Atılım Güneş and Backes, Dietmar},
	month = mar,
	year = {2021},
	pages = {7249},
}

About

ML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led by Trillium Technologies. It has also been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033).

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An ecosystem of data, models and code pipelines to tackle flooding with ML

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