Our open source Raster Vision project encapsulates a workflow for using deep learning to understand and analyze geospatial imagery. Raster Vision began with our work performing semantic segmentation on aerial imagery provided as part of international competitions.

Raster Vision machine learning
A ResNet FCN’s semantic segmentation as it becomes more accurate during training. Read more in this blog post

The functionality in Raster Vision can be made accessible through Raster Foundry, Azavea’s product to help users gain insight from geospatial data quickly, repeatably, and at any scale.


Raster Vision display of a single project

To date, Raster Vision has been used to automate inspection of assets with a drone, find center-pivot irrigation systems across an entire state, classify deforestation in the Amazon, and count cars in satellite imagery.

Raster Vision is released under an Apache 2.0 open source license and is being actively developed in the open on GitHub.