Machine learning models for end-to-end flood extent segmentation.
Project description
ML4Floods is an end-to-end ML pipeline for flood extent estimation: from data preprocessing, model training, model deployment to visualization.
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
spaceml-org.github.io/ml4floods
These tutorials may help you explore the datasets and models:
- Project rationale.
- Run the model on time series of Sentinel-2 images
- ML-models step by step
- Training
- Inference on new data (a Sentinel-2 image)
- Perf metrics
- Training
- Ingest data from Copernicus EMS
The WorldFloods database
The WorldFloods database contains 444 pairs of Sentinel-2 images and flood segmentation masks.
It requires approximately 300GB of hard-disk storage.
The WorldFloods database is released under a Creative Commons non-commercial licence
To download the WorldFloods database or the pretrained flood segmentation models for Sentinel-2 see the instructions to download the database.
Cite
If you find this work useful please cite:
@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},
}
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