Easy-EO documentation

Easy-EO is a Python package 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. It supports all rasterio supported datasets since it is built on rasterio.

Why Easy-EO?

Working directly with libraries like Rasterio can be powerful, but they often require verbose boilerplate code for simple operations such as raster reprojection, resampling, arithmetic between rasters, clipping, mosaicking, or plotting. Easy-EO abstracts these routines into high-level, chainable methods, allowing users to:

  • Perform multiple operations in a single, readable chain.

  • Persist intermediate results in memory without writing to disk unnecessarily.

  • Automatically align rasters with differing shapes or coordinate reference systems.

  • Return a consistent EEORasterDataset object from each operation, enabling further chaining.

  • Use terminal visualization methods for plotting bands, composites, and histograms, which do not return EEORasterDataset but instead display results.

Chainable Workflow

All methods in Easy-EO are designed to be chainable, except for visualization operations which are terminal. For 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)

Visualization is always done at the end of the chain:

# Terminal operation: display raster and histogram
result.plot_raster_with_histogram(bands=[1,2], stretch=True)

Key Features

  • Raster algebra: Supports pixel-wise addition, subtraction, multiplication, division, and power operations. Operator overloading allows +, -, *, /, and ** for concise syntax.

  • Raster indices: Compute normalized difference indices (e.g., NDVI) or custom indices, returning either NumPy arrays or EEORasterDataset for further chaining.

  • Spatial operations: Clip rasters using bounding boxes or vector geometries, mosaic multiple rasters, or stack rasters as new bands.

  • Standardization & normalization: Apply z-score, min-max, or percentile-based normalization.

  • Visualization: Plot individual bands, composites, histograms, or raster with histogram. Supports multi-band rasters and percentile-based contrast stretching.

Getting Started

See Getting Started for step-by-step instructions on loading rasters, performing arithmetic, computing indices, and visualizing results.

User Guide

API Reference