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Landsat ML Cookbook

Covers the essential materials for working with Landsat data in the context of machine learning workflows.
https://github.com/projectpythia/landsat-ml-cookbook

Category: Sustainable Development
Sub Category: Education

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Machine learning on Landsat satellite data using open source tools

README.md

Landsat 8

Landsat ML Cookbook

nightly-build
Binder
DOI

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
{kbd}Shift+{kbd}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:

  1. Clone the Landsat ML Cookbook repository:

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

  2. Move into the landsat-ml-cookbook directory

    cd landsat-ml-cookbook

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

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

  4. Move into the notebooks directory and start up Jupyterlab

    cd notebooks/
jupyter lab

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this cookbook, please cite it as below."
authors:
  # add additional entries for each author -- see https://github.com/citation-file-format/citation-file-format/blob/main/schema-guide.md
  - family-names: Roumis
    given-names: Demetris
    website: https://github.com/droumis
    orcid: https://orcid.org/0000-0003-4670-1657
  - name: "Landsat ML Cookbook contributors" # use the 'name' field to acknowledge organizations
    website: "https://github.com/ProjectPythia/landsat-ml-cookbook/graphs/contributors"
title: "Landsat ML Cookbook"
abstract: "Machine learning on Landsat data."

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Committers metadata

Last synced: 5 days ago

Total Commits: 105
Total Committers: 4
Avg Commits per committer: 26.25
Development Distribution Score (DDS): 0.21

Commits in past year: 1
Committers in past year: 1
Avg Commits per committer in past year: 1.0
Development Distribution Score (DDS) in past year: 0.0

Name Email Commits
Demetris Roumis r****d@g****m 83
Julia Kent 4****t 9
Robert Ford 5****d 7
Andrew Huang a****g@a****m 6

Committer domains:

Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 7
Total pull requests: 21
Average time to close issues: 4 months
Average time to close pull requests: 4 days
Total issue authors: 6
Total pull request authors: 5
Average comments per issue: 1.57
Average comments per pull request: 3.52
Merged pull request: 17
Bot issues: 0
Bot pull requests: 0

Past year issues: 1
Past year pull requests: 1
Past year average time to close issues: N/A
Past year average time to close pull requests: 21 minutes
Past year issue authors: 1
Past year pull request authors: 1
Past year average comments per issue: 1.0
Past year average comments per pull request: 1.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/projectpythia/landsat-ml-cookbook

Top Issue Authors

  • droumis (2)
  • rbavery (1)
  • dphow (1)
  • tylere (1)
  • sandhujasmine (1)
  • iuryt (1)

Top Pull Request Authors

  • droumis (11)
  • jukent (3)
  • ahuang11 (3)
  • brian-rose (2)
  • r-ford (2)

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Dependencies

.github/workflows/nightly-build.yaml actions
.github/workflows/publish-book.yaml actions
.github/workflows/trigger-book-build.yaml actions
.github/workflows/trigger-delete-preview.yaml actions
.github/workflows/trigger-link-check.yaml actions
.github/workflows/trigger-preview.yaml actions
environment.yml conda
  • bokeh <3.0
  • cartopy
  • colorcet
  • dask
  • dask-ml
  • datashader
  • geoviews <1.10
  • hvplot
  • intake
  • intake-xarray <0.7
  • ipykernel
  • jupyter-book
  • jupyter_server <2
  • jupyterlab
  • numpy
  • odc-stac
  • pandas
  • panel
  • pip
  • planetary-computer
  • pyopenssl >22
  • pystac
  • pystac-client
  • python 3.10.*
  • rasterio
  • s3fs
  • shapely <2.0.0
  • xarray 2023.04.*
  • xarray-datatree

Score: 4.276666119016055