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CosmiQ Works Geospatial Machine Learning Analysis Toolkit
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An open source ML pipeline for overhead imagery by CosmiQ Works

PyPI python version PyPI build docs license

This package is under active development. Check back soon for updates!

This repository provides the source code for the CosmiQ Works solaris project, which provides software tools for:

  • Tiling large-format overhead images and vector labels
  • Converting between geospatial raster and vector formats and machine learning-compatible formats
  • Performing semantic and instance segmentation, object detection, and related tasks using deep learning models designed specifically for overhead image analysis
  • Evaluating performance of deep learning model predictions

Installation Instructions

We recommend creating a conda environment with the dependencies defined in environment.yml before installing solaris. After cloning the repository:

cd solaris
conda env create -n solaris -f environment.yml
conda activate solaris
pip install .


The package also exists on PyPI, but note that some of the dependencies, specifically rtree and gdal, are challenging to install without anaconda. We therefore recommend installing at least those dependency using conda before installing from PyPI.

conda install -c conda-forge rtree gdal=2.4.1

If you don't want to use conda, you can install libspatialindex, then pip install rtree. Installing GDAL without conda can be very difficult and approaches vary dramatically depending upon the build environment and version, but online resources may help with specific use cases.

Once you have that dependency set up, install as usual using pip:

pip install solaris


All dependencies can be found in the docker file Dockerfile or environment.yml



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