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Notebook","category":"Climate Change","sub_category":"Natural Hazard and Storms","monthly_downloads":103,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"\n[![Article DOI:10.1038/s41598-023-47595-7](https://img.shields.io/badge/Article%20DOI-10.1038%2Fs41598.023.47595.7-blue)](https://doi.org/10.1038/s41598-023-47595-7)  [![PyPI](https://img.shields.io/pypi/v/ml4floods)](https://pypi.org/project/ml4floods/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ml4floods)](https://pypi.org/project/ml4floods/) [![PyPI - License](https://img.shields.io/pypi/l/ml4floods)](https://github.com/spaceml-org/ml4floods/blob/main/LICENSE) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8153514.svg)](https://doi.org/10.5281/zenodo.8153514) [![docs](https://badgen.net/badge/docs/spaceml-org.github.io%2Fml4floods/blue)](https://spaceml-org.github.io/ml4floods/)\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/spaceml-org/ml4floods/main/jupyterbook/ml4floods_banner.png\" alt=\"awesome ml4floods\" width=\"50%\"\u003e\n\u003c/p\u003e\n\nML4Floods is an end-to-end ML pipeline for flood extent estimation: from data preprocessing, model training, model deployment to visualization. Here you can find the [WorldFloodsV2🌊 dataset](https://spaceml-org.github.io/ml4floods/content/worldfloods_dataset.html) and [trained models 🤗](https://huggingface.co/isp-uv-es/ml4floods) for flood extent estimation in Sentinel-2 and Landsat.\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/spaceml-org/ml4floods/main/jupyterbook/content/ml4ops/ts_albania.gif\" alt=\"awesome flood extent estimation\" width=\"100%\"\u003e\n\u003c/p\u003e\n\n## Install\n\nInstall from [pip](https://pypi.org/project/ml4floods/):\n\n```bash\npip install ml4floods\n```\n\nInstall the latest version from GitHub:\n\n```bash\npip install git+https://github.com/spaceml-org/ml4floods#egg=ml4floods\n```\n\n## Docs \n\n[![docs](https://badgen.net/badge/docs/spaceml-org.github.io%2Fml4floods/blue)](https://spaceml-org.github.io/ml4floods/)\n\nThese tutorials may help you explore the datasets and models:\n* [Kherson Dam Break *end-to-end* flood extent map](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_postprocess_inference.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/spaceml-org/ml4floods/blob/main/jupyterbook/content/ml4ops/HOWTO_postprocess_inference.ipynb)\n* [Run the model on time series of Sentinel-2 images](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_inference_on_image_time_series.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/spaceml-org/ml4floods/blob/main/jupyterbook/content/ml4ops/HOWTO_inference_on_image_time_series.ipynb)\n* [Ingest data from Copernicus EMS](https://spaceml-org.github.io/ml4floods/content/prep/full_data_ingest.html)  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/spaceml-org/ml4floods/blob/main/jupyterbook/content/prep/full_data_ingest.ipynb)\n* [ML-models step by step](https://spaceml-org.github.io/ml4floods/content/ml_overview.html)\n    * [Training](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_Train_models.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/spaceml-org/ml4floods/blob/main/jupyterbook/content/ml4ops/HOWTO_Train_models.ipynb)\n    * [Inference on new data](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_Run_Inference_on_new_data.html) (a Sentinel-2 image) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/spaceml-org/ml4floods/blob/main/jupyterbook/content/ml4ops/HOWTO_Run_Inference_on_new_data.ipynb)\n    * [Perf metrics](https://spaceml-org.github.io/ml4floods/content/ml4ops/HOWTO_performance_metrics_workflow.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/spaceml-org/ml4floods/blob/main/jupyterbook/content/ml4ops/HOWTO_performance_metrics_workflow.ipynb)\n\n## The *WorldFloods* database\n [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8153514.svg)](https://doi.org/10.5281/zenodo.8153514)\n \nThe [*WorldFloods* database](https://www.nature.com/articles/s41598-023-47595-7) contains 509 pairs of Sentinel-2 images and flood segmentation masks. \nIt requires approximately 76GB of hard-disk storage. \n\nThe *WorldFloods* database and all pre-trained models are released under a [Creative Commons non-commercial licence](https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt) \n\u003cimg src=\"https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by-nc.png\" alt=\"licence\" width=\"60\"/\u003e\n\nTo download the *WorldFloods* database or the pretrained flood segmentation models see [the instructions to download the database](https://spaceml-org.github.io/ml4floods/content/worldfloods_dataset.html).\n\n## Cite\n\nIf you find this work useful please cite:\n\n```\n@article{portales-julia_global_2023,\n\ttitle = {Global flood extent segmentation in optical satellite images},\n\tvolume = {13},\n\tissn = {2045-2322},\n\tdoi = {10.1038/s41598-023-47595-7},\n\tnumber = {1},\n\turldate = {2023-11-30},\n\tjournal = {Scientific Reports},\n\tauthor = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {20316},\n}\n@article{mateo-garcia_towards_2021,\n\ttitle = {Towards global flood mapping onboard low cost satellites with machine learning},\n\tvolume = {11},\n\tissn = {2045-2322},\n\tdoi = {10.1038/s41598-021-86650-z},\n\tnumber = {1},\n\turldate = {2021-04-01},\n\tjournal = {Scientific Reports},\n\tauthor = {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},\n\tmonth = mar,\n\tyear = {2021},\n\tpages = {7249},\n}\n```\n\n## About\n\nML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led by [Trillium Technologies](http://trillium.tech/). It has also been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033).\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1038/s41598-023-47595-7","https://doi.org/10.5281/zenodo.8153514"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/1110","html_url":"https://ost.ecosyste.ms/projects/1110"}