Raster Vision: A New Open Source Framework for Deep Learning on Satellite and Aerial Imagery

An open source framework for deep learning on satellite and aerial imagery. https://docs.rastervision.io

https://github.com/azavea/raster-vision

What is Raster Vision?

Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets, including non-georeferenced data like oblique drone imagery. It allows engineers to quickly and repeatably configure experiments that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment.
The input to a Raster Vision workflow is a set of images and training data, optionally with Areas of Interest (AOIs), that describe where the images are labeled. Running a workflow results in evaluation metrics and a packaged model and configuration that enables easy deployment. Raster Vision also supports running multiple experiments at once to find the best model (or models) to deploy.