Easy-EO

Easy-EO logo

Easy-EO is a lightweight, extensible Python library for raster-based Earth Observation (EO) analysis which allows for chainable raster processing, algebra, and visualization. It provides high-level abstractions over libraries such as Rasterio, NumPy, and Matplotlib, enabling users to perform common earth-observation analyses and visualization tasks efficiently, without dealing with the underlying complexity.

Features

Raster operations with spatial awareness
Algebraic operations (add, subtract, multiply, divide)
Spectral index library (ndvi, ndwi, ndmi, ndbi, evi, savi)
Resampling, reprojection, and alignment
Clip rasters using vectors or bounding boxes
Normalization (min-max, percentile, z-score)
Named bands - address bands by name anywhere an index is accepted
STAC data access - search and load Sentinel-2/Landsat scenes, reading only the area of interest
xarray interoperability (to_xarray() / from_xarray())
Hosted sample dataset, one call away (eeo.datasets.load_sample_dataset())
Visualization helpers (bands, composites, histograms)
Backend-aware design (currently NumPy and Rasterio)

Installation

conda create -n env_name python=3.10
conda activate env_name
pip install easy-eo

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.

Supported Backends

Backend	Description
NumPy	Fast, in-memory arrays without I/O
Rasterio	Full geospatial support (CRS, transform, resampling)

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

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