Raster Vision
An open source Python library and framework for building computer vision models on satellite, aerial, and large imagery sets.
What is Raster Vision?
Raster Vision is an open-source Python library and framework for building and deploying computer vision models on satellite, aerial, and other large-scale geospatial imagery. It solves the problem of applying deep learning to geo-referenced data by providing specialized tools for reading, processing, and outputting predictions in geospatial formats, supporting tasks like chip classification, object detection, and semantic segmentation.
Geospatial analysts, data scientists, and developers working with satellite or aerial imagery who need to implement deep learning workflows, as well as organizations seeking reproducible and scalable analysis of large imagery sets.
Developers choose Raster Vision because it offers a complete, specialized toolkit for geospatial deep learning, combining the flexibility of a library with the ease of a low-code framework, and includes built-in cloud integration for scalable processing.
Overview
An open source library and framework for deep learning on satellite and aerial imagery.
Use Cases
Best For
- Training object detection models on satellite imagery to identify buildings or vehicles
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