rastereasy 0.1.0

A library to ease the manipulation of geospatial images

Introduction

rastereasy is a Python library for simple manipulation of georeferenced images (*.tif, *.jp2, *.shp, ...). The goal is to simplify geospatial workflows by offering tools for reading and processing raster and vector files, resampling, cropping, reprojecting, stacking, etc of raster images, easy visualizations such as color composites and spectral plots, use (train / apply) some classical Machine Learning algorithms on images, provide some tools for late fusion of classifications (Dempster-Shafer), ...

The main class, Geoimage, enables to process raster similarly than numpy arrays while keeping and adapting all meta data.

Documentation

A complete documentation can be found here

Example Usage

Here's a quick example of what you can do with rastereasy:

 import rastereasy

 # Load a georeferenced image
 image = rastereasy.Geoimage("example.tif")

 # Get image information
 image.info()

 # Print value of pixel [100,200]
 print(image[100,200])

 # Create a color composite
 image.colorcomp(['4', '3', '2'])

 # Resample and reproject
 image_resampled = image.resampling(2)
 image_reproject = image.reproject("EPSG:4326")

 # Save the processed image
 image.save("processed_image.tif")

Installation

With pip

Install rastereasy via pip (the easiest method):

 $ pip install rastereasy

From source

To install rastereasy from source:

1. Clone the repository from GitHub:

$ git clone https://github.com/pythonraster/rastereasy.git

2. Navigate to the repository's root directory:

$ cd rastereasy

3. Install the package using pip:

$ pip install .

Interactive Visualization Notes

As illustrated, rastereasy supports interactive plotting of spectral bands for individual pixels. To enable this functionality in Jupyter Notebook, rastereasy installs some Jupyter extensions in your environment. You then need to rebuild jupyter by the command:

jupyter lab build

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To do

Check conda installation

Authors

• Thomas Corpetti
• Pierrick Matelot
• Augustin de la Brosse
• Candide Lissak

Citation

If you use rastereasy, please cite:

Thomas Corpetti, Pierrick Matelot, Augustin de la Brosse, Candide Lissak Rastereasy: A Python package for an easy manipulation of remote sensing images Journal of Open Source Software, submitted, 2025.

License

This project is licensed under the MIT License – see the LICENCE file for details.