Raster Foundry Archives | Page 2 of 4

Slider for testing seams of labeled machine learning data.

3 Ways to Analyze the Results of a Supervised Machine Learning Model

By Joe Morrison on December 13th, 2019

How accurate are our supervised machine learning models and what are they really doing? We offer 3 tips to help you better understand these models.

Azavea team team leading GIS training in Guyana.

Software Engineering

Ethical Machine Learning for Disaster Relief: Rage for Machine Learning

By Niki LaGrone on December 10th, 2019

ML's predictive powers are driving a rage for deep learning in the crisis management and disaster relief industries. How can these powers be harnessed for ethical machine learning?

Software Engineering

Ethical Machine Learning for Disaster Relief: Avoiding the Second Disaster

By Niki LaGrone on November 25th, 2019

Machine learning on satellite imagery is revolutionizing disaster relief. What does ethical machine learning mean in this field?

Software Engineering

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.

Viridis color scheme applied to analysis of part of the U.S.

Software Engineering

Introducing WMS and WCS Support for GeoTrellis and Raster Foundry

By Nathan Zimmerman on July 3rd, 2019

Raster Foundry and GeoTrellis now support WMS and WCS standards, enabling streamlined workflows that utilize multiple data sources.