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--- |
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license: cc-by-nc-4.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- remote sensing |
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- sentinel2 |
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- landsat |
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- floods |
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--- |
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# ml4floods trained models |
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This repository contains the trained models of the publication: |
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> 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. |
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We include the trained models: |
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* **Unet multioutput** in folder `models/WF2_unetv2_all` |
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* **Unet multioutput S2-to-L8** in folder `models/WF2_unetv2_bgriswirs` |
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* **Unet multioutput RGBNIR** in folder `models/WF2_unetv2_rgbi` |
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The following table shows the performance of the models in the test dataset: |
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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. |
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If you find this work useful please cite: |
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``` |
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@article{portales-julia_global_2023, |
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title = {Global flood extent segmentation in optical satellite images}, |
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volume = {13}, |
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issn = {2045-2322}, |
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doi = {10.1038/s41598-023-47595-7}, |
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number = {1}, |
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urldate = {2023-11-30}, |
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journal = {Scientific Reports}, |
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author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis}, |
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month = nov, |
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year = {2023}, |
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pages = {20316}, |
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} |
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``` |
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## Licence |
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<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by-nc.png" alt="licence" width="60"/> |
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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) |
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The ML4Floods python package is published under a [GNU Lesser GPL v3 licence](https://www.gnu.org/licenses/lgpl-3.0.en.html) |
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## Acknowledgments |
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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). |
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<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"> |
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