Raster Vision Archives | Page 2 of 3

STAC: Creating an Ecosystem of SpatioTemporal Assets

By Niki LaGrone, James Santucci and Aaron Su on October 1st, 2019

STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.

Company

Is This a Crosswalk?: 5 Takeaways from Training a Data Labeling Team

By Niki LaGrone on September 13th, 2019

We trained remote computer vision workers to provide data labeling for machine learning projects. Here's what we learned.

Ground truth imperfectly overlaying machine learning predictions.

Software Engineering

Using Noisy Labels to Train Deep Learning Models on Satellite Imagery

By Lewis Fishgold on August 5th, 2019

How do noisy labels affect the accuracy of a deep learning model? We added different amounts of noise to the SpaceNet Vegas buildings dataset and trained some models to find out.

Picture of dice overlaid with charts from Anscombe's Quartet.

Software Engineering

An Introduction to Machine Learning Accuracy Metrics

By Joe Morrison on July 31st, 2019

How accurate of a machine learning model can you make? It depends on how we decide to define "accurate".

Raster Vision applied to the city of Georgetown, Guyana.

Software Engineering

Raster Vision 0.9 Release Candidate

By Lewis Fishgold on April 15th, 2019

This release of Raster Vision includes bug fixes, an easier setup, improved performance, and the ability to train models off of labels in OSM.

Aster Vision badge of a rocket shooting into space overtop a photograph of the night sky.

Software Engineering

Aster Vision – A New Open Source Framework for Deep Learning on Astrospatial Imagery and Space Exploration

By James Santucci on April 1st, 2019

Aster Vision is an open source machine learning library for analyzing huge troves of astrospatial data and finding habitable planets around nearby stars.