ml4floods

ml4floods trained models

This repository contains the trained models of the publication:

E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova Global flood extent segmentation in optical satellite images. Scientific Reports 13, 20316 (2023). DOI: 10.1038/s41598-023-47595-7.

We include the trained models:

• Unet multioutput in folder models/WF2_unetv2_all
• Unet multioutput S2-to-L8 in folder models/WF2_unetv2_bgriswirs
• Unet multioutput RGBNIR in folder models/WF2_unetv2_rgbi

The following table shows the performance of the models in the test dataset:

In order to run any of these models in a Landsat or Sentinel-2 scene see the tutorial Inference with clouds aware floods segmentation model in the ml4floods docs.

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},
}

Licence

<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by-nc.png" alt="licence" width="60"/>

All pre-trained models in this repository are released under a Creative Commons non-commercial licence

The ML4Floods python package is published under a GNU Lesser GPL v3 licence

Acknowledgments

This research has been supported by the DEEPCLOUD project (PID2019-109026RB-I00) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Union (NextGenerationEU). <img width="300" title="DEEPCLOUD project (PID2019-109026RB-I00, University of Valencia) funded by MCIN/AEI/10.13039/501100011033." alt="DEEPCLOUD project (PID2019-109026RB-I00, University of Valencia) funded by MCIN/AEI/10.13039/501100011033." src="https://www.uv.es/chovago/logos/logoMICIN.jpg">