ML4Floods is an end-to-end ML pipeline for flood extent estimation: from data preprocessing, model training, model deployment to visualization. Here you can find the WorldFloodsV2🌊 dataset and trained models 🤗 for flood extent estimation in Sentinel-2 and Landsat.
Install from pip:
pip install ml4floodsInstall the latest version from GitHub:
pip install git+https://github.com/spaceml-org/ml4floods#egg=ml4floodsThese tutorials may help you explore the datasets and models:
- Kherson Dam Break end-to-end flood extent map
- Run the model on time series of Sentinel-2 images
- Ingest data from Copernicus EMS
- ML-models step by step
- Training
- Inference on new data (a Sentinel-2 image)
- Perf metrics
- Training
The WorldFloods database contains 509 pairs of Sentinel-2 images and flood segmentation masks. It requires approximately 76GB of hard-disk storage.
The WorldFloods database and all pre-trained models are released under a Creative Commons non-commercial licence
To download the WorldFloods database or the pretrained flood segmentation models see the instructions to download the database.
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},
}
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).