Easy-EO Tutorials

Code, notebooks and data for the Easy-EO video series.

Library: Tommy-Burns/easy-eo · Docs: easy-eo.readthedocs.io · Channel: @Tommy's Codebase

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Start here

If you have never touched a GeoTIFF, watch Episode 1 and run this:

import eeo
from eeo.datasets import load_sample_dataset

sd = load_sample_dataset()  # downloads + caches a hosted Sentinel-2 subset

scene = eeo.load_raster(sd.sentinel2_cog_stacked)   # red, green, blue, nir
scene.ndvi(red="red", nir="nir").plot_raster(cmap="RdYlGn")

That is a real NDVI map of a real Sentinel-2 scene, in five lines, with no GDAL setup and no downloads to manage by hand.

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Setup

Pick one package manager and stay with it. Mixing pip and conda in the same environment can break geospatial stacks. I recommend installing it into an environment created with conda conda create -n eeo-env python=3.12 (easy-eo requires Python 3.10+) instead of using a regular Python environment python -m venv .venv.
After creating the conda environment, proceed to install easy-eo

conda (recommended for GDAL-based stacks):

conda activate eeo-env
conda install -c conda-forge easy-eo jupyterlab

# easy-eo provides a STAC extra, install it via:
conda install -c conda-forge pystac-client planetary-computer
# easy-eo provides an xarray extra, install it via:
conda install -c conda-forge easy-eo xarray rioxarray

pip:
Using pip in a conda environment

conda activate eeo-env
pip install easy-eo jupyterlab

# easy-eo provides a STAC extra, install it via:
pip install "easy-eo[stac]"
# easy-eo provides an xarray extra, install it via:
pip install "easy-eo[xarray]"

# or install both extras with
pip install "easy-eo[stac,xarray]"

easy-eo requires Python 3.10+. Verify installation with:

import eeo; eeo.show_versions()

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Following along

• Pause on the code cells. Every cell in the notebook is the cell in the video, in the same order, so you can catch up without scrubbing.
• Stuck? Open an issue here with the episode number and the full traceback.
• Bug in the library itself? That belongs on easy-eo/issues. I read both.
• Want an episode on something? Open a Discussion.

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About Easy-EO

Easy-EO is a lightweight, extensible Python library for raster Earth Observation: chainable processing, band algebra, spectral indices and visualization, without the GDAL boilerplate. It applies one written-down nodata and dtype contract to every operation, ships type hints (py.typed), and is tested on Python 3.10–3.14 across Linux, macOS and Windows.

If the series is useful, ⭐ the library repo.

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License

• Notebooks and code in this repo: MIT (see LICENSE).
• Satellite imagery belongs to its providers:
  • Sentinel-2 data is © Copernicus / ESA, and
  • Copernicus DEM is © ESA / Airbus.

Attribution notes are in each notebook.