Easy-EO

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Easy-EO is a lightweight, extensible Python library for raster-based Earth Observation (EO) analysis: chainable raster processing, band algebra, spectral indices, and visualization, in a few readable lines instead of dealing with complex boilerplate code.

From satellite archive to NDVI map

import eeo

results = eeo.stac_search(
    "sentinel-2-l2a",
    bbox=(11.0, 46.5, 11.2, 46.7),        # area of interest, WGS 84 lon/lat
    datetime="2023-06-01/2023-08-31",
    cloud_cover=20,
    limit=1,
)
scene = results[0].load(["B04", "B08"])   # reads only the area of interest
ndvi = scene.ndvi(red="B04", nir="B08")
ndvi.plot_raster()

That is the whole workflow - no scene downloads, no GDAL wrangling. The search queries Microsoft Planetary Computer (any STAC catalog works, you have to pass catalog="your stac catalog"), and the load streams just the window covering your bounding box over HTTP: 14 MB out of a 240 MB Sentinel-2 tile, in a few seconds.

The stac_search needs the STAC extra - pip install "easy-eo[stac]", or the conda equivalent. If you prefer to start offline, jump to the hosted sample dataset, which needs no network after the first call.

Features

Before and after

One ordinary task: clip a 4-band scene to an area of interest held in a vector file, compute NDVI, save it as a GeoTIFF. Both versions below run as written, against the same hosted sample dataset

• a 1024x1024 Sentinel-2 subset and a boundary polygon - so you can paste either one and watch it work.

Here it is in raw Rasterio, GeoPandas and NumPy, with Easy-EO not installed at all -

import geopandas as gpd
import numpy as np
import rasterio
from rasterio.mask import mask

BASE = "https://github.com/Tommy-Burns/easy-eo/releases/download/sample-data-v1/"

with rasterio.open(BASE + "sentinel2_small_cog.tif") as src:
    aoi = gpd.read_file(BASE + "roi.gpkg").to_crs(src.crs)
    clipped, transform = mask(src, aoi.geometry.values, crop=True)
    bands = {name: i for i, name in enumerate(src.descriptions)}
    nodata = src.nodata
    profile = src.profile

red = clipped[bands["red"]].astype("float32")
nir = clipped[bands["nir"]].astype("float32")

valid = (red != nodata) & (nir != nodata)
total = nir + red
ndvi = np.where(valid & (total != 0), (nir - red) / np.where(total == 0, 1, total), 0.0)
ndvi = np.where(valid, ndvi, np.nan).astype("float32")

profile.update(
    count=1, dtype="float32", nodata=np.nan,
    height=ndvi.shape[0], width=ndvi.shape[1], transform=transform,
)
with rasterio.open("ndvi.tif", "w", **profile) as dst:
    dst.write(ndvi, 1)

-- and in Easy-EO, where load_sample_dataset() fetches the same two files and caches them:

import eeo
from eeo.datasets import load_sample_dataset

sd = load_sample_dataset()

(
    eeo.load_raster(sd.sentinel2_cog_stacked)
    .clip_raster_with_vector(sd.boundary)
    .ndvi(red="red", nir="nir")
    .save_raster("ndvi.tif")
)

Both blocks produce byte-identical output - same shape, CRS, transform, nodata, and every one of the pixel values, including all pixels the clip masks away (approx. a quarter of the image). So the point is not the line count. It is that Rasterio makes you take four decisions by hand, each one a chance to be quietly wrong: reprojecting the AOI into the raster's CRS (the sample boundary is lon/lat, the scene is UTM), mapping band names to indices, masking nodata before the arithmetic, and rebuilding the output profile. Drop just the mask and NDVI comes out as 0.0 across the clipped-away quarter of the image - a value that looks like bare ground in your statistics and your plot, not like missing data.

Easy-EO applies those same rules for you, consistently, on every operation. They are written down in the nodata and dtype contract and each one is backed by tests.

What's next

Easy-EO is built around one scene at a time, and everything above works that way today. These are the next capabilities, in the order they are being built:

Already using xarray? You do not have to choose. to_xarray() and from_xarray() convert in both directions, so you can clip and compute indices here, hand the result to dask or anything else in the xarray ecosystem, and come back - which is also how to work past a single machine's memory today. See the xarray interop guide.

Installation

Python 3.10 or newer, from either package manager:

pip install easy-eo

conda install -c conda-forge easy-eo

That is everything you need for the core: raster I/O, algebra, indices, preprocessing and plotting. Two heavier integrations are kept separate, so you only install them if you use them:

pip extras compose - pip install "easy-eo[stac,xarray]" installs both. conda has no concept of extras, so conda install "easy-eo[stac]" is not a valid command; the same packages are simply installed by name, as above.

Use one package manager, not both. If Easy-EO came from conda, install the extras from conda too. conda's solver knows nothing about pip-installed files, so a later conda install or conda update can overwrite them or leave a second copy of a shared dependency in the environment. Every extra dependency is on conda-forge, so there is no reason to mix.

Without an extra installed, the features that need it raise a MissingDependencyError telling you exactly what to install - nothing fails silently at import time.

Quick Example

from eeo import load_raster

ds_nir = load_raster("path/to/nir.tif")
ds_red = load_raster("path/to/red.tif")

# Chainable example: clip -> resample -> compute NDVI -> multiply
result = (
    ds_nir.clip_raster_with_bbox((0, 0, 1000, 1000))
    .resample(scale_factor=2)
    .normalized_difference(ds_red)
    .multiply(100)
)

Or try with a hosted sample data

from eeo.datasets import load_sample_dataset
from eeo import load_raster

sd = load_sample_dataset()

scene = load_raster(sd.sentinel2_cog_stacked)  # red, green, blue, nir bands

ndvi = scene.ndvi(red="red", nir="nir")
ndvi.plot_raster()

Tutorials

Sixteen runnable notebooks live in examples/, from first install through to complete analyses (flood mapping, drought stress, land cover, terrain). Each one opens in Colab with no local setup — the first cell installs Easy-EO when it detects Colab:

Quickstart: NDVI — open a scene, compute an index, plot it
Search and load from STAC — find real scenes, read them over HTTP
Flood mapping with NDWI — Pakistan 2022, before/after, area affected

The full index, including what each notebook covers, is in examples/README.md and in the tutorials page of the documentation.

Gallery

Every image below is straight out of an Easy-EO plotting call on the sample dataset, with the library's own defaults - no touch-ups. Regenerate them all with python scripts/build_gallery.py.

scene.plot_composite(["red", "green", "blue"])	scene.plot_composite(["nir", "red", "green"])
scene.ndvi(red="red", nir="nir", name="NDVI").plot_raster(cmap="RdYlGn")	dem.plot_raster(cmap="Spectral_r") - the same call on a DEM
scene.plot_histogram() - every band at once	clipped.plot_raster_with_histogram(cmap="RdYlGn")

Bands are addressed by name throughout ("red", "nir") because the sample carries band descriptions; a 1-based index works anywhere a name does.

Supported Backends

Documentation

📚 Full documentation is available at:

👉 Easy-EO Documentation

Project Status

🚧 Active development The API is stabilizing but may change before v1.0.

Contributing

Contributions are welcome!

• Bug reports
• Feature requests
• Documentation improvements

Please open an issue or pull request on GitHub.

License

MIT License © 2025 Thomas Burns Botchwey