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Notebook","category":"Sustainable Development","sub_category":"Education","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Landsat ML Cookbook\n\n![Landsat 8](./notebooks/images/nasa_landsat8.jpg \"Landsat 8\")\n\n[![nightly-build](https://github.com/ProjectPythia/landsat-ml-cookbook/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/landsat-ml-cookbook/actions/workflows/nightly-build.yaml)\n[![Binder](http://binder.projectpythia.org/badge_logo.svg)](http://binder.projectpythia.org/v2/gh/ProjectPythia/landsat-ml-cookbook/main?labpath=notebooks)\n[![DOI](https://zenodo.org/badge/563445694.svg)](https://zenodo.org/badge/latestdoi/563445694)\n\nThis Project Pythia Cookbook covers the essential materials for working with Landsat data in the context of machine learning workflows.\n\n## Motivation\n\nOnce you complete this cookbook, you will have the skills to access, resample, regrid, reshape, and rescale satellite data, as well as the foundation for applying machine learning to it. You will also learn how to interactively visualize your data at every step in the process.\n\n## Authors\n\n[Demetris Roumis](https://github.com/droumis)\n[Andrew Huang](https://github.com/ahuang11)\n\n### Contributors\n\n\u003ca href=\"https://github.com/ProjectPythia/landsat-ml-cookbook/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=ProjectPythia/landsat-ml-cookbook\" /\u003e\n\u003c/a\u003e\n\n\nThis cookbook was initially inspired by the [EarthML](https://github.com/pyviz-topics/EarthML) . See a list of the EarthML contributors [here:](https://github.com/pyviz-topics/EarthML/graphs/contributors)\n\u003ca href=\"https://github.com/pyviz-topics/EarthML/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=pyviz-topics/EarthML\" /\u003e\n\u003c/a\u003e\n\n## Structure\nThis cookbook is broken up into two main sections - \"Foundations\" and \"Example Workflows.\"\n\n### Foundations\nThe foundational content includes:\n- Start Here - Introduction to Landsat data.\n- Data Ingestion - Geospatial-Specific Tooling - Demonstrating a method for loading and accessing Landsat data from Microsoft's Planetary Computer platform with tooling from pystac and odc.\n- Data Ingestion - General Purpose Tooling - Demonstrating approaches for domain-independent data access using Intake.\n\n### Example Workflows\nExample workflows include:\n- Spectral Clustering - Demonstrating a machine learning approach to cluster pixels of satellite data and comparing cluster results across time\n\n## Running the Notebooks\nYou can either run the notebook using [Binder](https://binder.projectpythia.org/) or on your local machine.\n\n### Running on Binder\n\nThe simplest way to interact with a Jupyter Notebook is through\n[Binder](https://binder.projectpythia.org/), which enables the execution of a\n[Jupyter Book](https://jupyterbook.org) in the cloud. The details of how this works are not\nimportant for now. All you need to know is how to launch a Pythia\nCookbooks chapter via Binder. Simply navigate your mouse to\nthe top right corner of the book chapter you are viewing and click\non the rocket ship icon, (see figure below), and be sure to select\n“launch Binder”. After a moment you should be presented with a\nnotebook that you can interact with. I.e. you’ll be able to execute\nand even change the example programs. You’ll see that the code cells\nhave no output at first, until you execute them by pressing\n{kbd}`Shift`\\+{kbd}`Enter`. Complete details on how to interact with\na live Jupyter notebook are described in [Getting Started with\nJupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter).\n\n### Running on Your Own Machine\nIf you are interested in running this material locally on your computer, you will need to follow this workflow:\n\n1. Clone the Landsat ML Cookbook repository:\n\n   ```bash\n    git clone https://github.com/ProjectPythia/landsat-ml-cookbook.git\n    ```  \n1. Move into the `landsat-ml-cookbook` directory\n    ```bash\n    cd landsat-ml-cookbook\n    ```  \n1. Create and activate your conda environment from the `environment.yml` file\n    ```bash\n    conda env create -f environment.yml\n    conda activate landsat-ml-cookbook\n    ```  \n1.  Move into the `notebooks` directory and start up Jupyterlab\n    ```bash\n    cd notebooks/\n    jupyter lab\n    ```\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["earth-observation"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/308114","html_url":"https://ost.ecosyste.ms/projects/308114"}