This Project Pythia Cookbook covers the essential materials for working with Landsat data in the context of machine learning workflows.
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.
This cookbook was initially inspired by the EarthML . See a list of the EarthML contributors here:
This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."
The foundational content includes:
Example workflows include:
You can either run the notebook using Binder or on your local machine.
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
{kbd}Shift
+{kbd}Enter
. Complete details on how to interact with
a live Jupyter notebook are described in Getting Started with
Jupyter.
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
landsat-ml-cookbook
directory
cd landsat-ml-cookbook
environment.yml
file
conda env create -f environment.yml
conda activate landsat-ml-cookbook
notebooks
directory and start up Jupyterlab
cd notebooks/
jupyter lab