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Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines 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 pipeline is a set of images and training data, optionally with Areas of Interest (AOIs) that describe where the images are labeled. The output of a Raster Vision pipeline is a model bundle that allows you to easily utilize models in various deployment scenarios.
Features
- Gather dataset-level statistics and metrics for use in downstream processes
- Create training chips from a variety of image and label sources
- Train a model using a “backend” such as PyTorch
- Make predictions using trained models on validation and test data
- Derive evaluation metrics such as F1 score, precision and recall against the model’s predictions on validation datasets
- Bundle the trained model and associated configuration into a model bundle, which can be deployed in batch processes, live servers, and other workflows
Project Samples
