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

Install the package:

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

These tutorials may help you explore the datasets and models:

• Project rationale.
• Data Preprocessing
• ML-based flood segmentation models
  • Training
  • Inference on new data (a Sentinel-2 image)
  • Perf metrics

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},
}