---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- remote sensing
- sentinel2
- landsat
- floods
---
# 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](https://www.nature.com/articles/s41598-023-47595-7). _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*](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_Run_Inference_multioutput_binary.html) 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
All pre-trained models in this repository are released under a [Creative Commons non-commercial licence](https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt)
The ML4Floods python package is published under a [GNU Lesser GPL v3 licence](https://www.gnu.org/licenses/lgpl-3.0.en.html)
## 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).
