Landsat 8

Landsat ML Cookbook

This Project Pythia Cookbook covers the essential materials for working with Landsat data in the context of machine learning workflows.

Motivation

Once 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.

Authors

Demetris Roumis Andrew Huang

Contributors

This cookbook was initially inspired by the EarthML . See a list of the EarthML contributors here:

Structure

This cookbook is broken up into two main sections - “Foundations” and “Example Workflows.”

Foundations

The foundational content includes:

Start Here - Introduction to Landsat data.
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.
Data Ingestion - General Purpose Tooling - Demonstrating approaches for domain-independent data access using Intake.

Example Workflows

Example workflows include:

Spectral Clustering - Demonstrating a machine learning approach to cluster pixels of satellite data and comparing cluster results across time

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing Shift+Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

Clone the Landsat ML Cookbook repository:

 git clone https://github.com/ProjectPythia/landsat-ml-cookbook.git

Move into the landsat-ml-cookbook directory
```
cd landsat-ml-cookbook
```

Create and activate your conda environment from the environment.yml file

conda env create -f environment.yml
conda activate landsat-ml-cookbook

Move into the notebooks directory and start up Jupyterlab
```
cd notebooks/
jupyter lab
```