ML4Floods: an ecosystem of data, models and code pipelines to tackle flooding with ML

About

ML4Floods is an open-source project funded by the United Kingdom Space Agency (UKSA) and led by Trillium Technologies. The project is hosted in GitHub, it contains a python package with all the necessary tools for training and deploying flood extent segmentation models for Sentinel-2. These tools include: image downloading, flood map acquisition, neural network training, testing and the visualization of the results in an interactive map. See the introduction for a more detailed explanation.

This work is an extension of the FDL Europe 2019 “Disaster Prevention, Progress and Response” team which results are published in:

G. Mateo-Garcia, J. Veitch-Michaelis, L. Smith, S. Oprea, G. Schumann, Y. Gal, Baydin G.A., Backes D. Towards global flood mapping onboard low cost satellites with machine learning. Scientific Reports 11, 7249 (2021).

This work is also part of the SpaceML. SpaceML contains a set of tools for “building the Machine Learning (ML) infrastructure needed to streamline and super-charge intelligent applications, automation and robotics needed to explore deep space and better manage our planetary spaceship for mutual benefit”.

Contributors

Gonzalo Mateo-García, Enrique Portalés-Julià, J. Emmanuel Jonhson, Nadia Ahmed, Sam Budd, Satyarth Praveen, Lucas Kruitwagen, Margaret Maynard-Reid, Nicholas Roth, Cormac Purcell, Richard Strange, Leo Silverberg, Guy Schumann, Edoardo Nemni, Luis Gómez-Chova, Freddie Kalaitzis, Sara Jennings, Jodie Hughes and James Parr.

Citation

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},
}