GroundWork Launches Campaigns
By Niki LaGrone on March 31st, 2021
Need to create a training dataset that contains multiple images? GroundWork launches "Campaigns" to help you handle large datasets for machine learning.
Cloud Detection in Satellite Imagery
By James McClain on February 8th, 2021
We compare the results of two machine learning models that detect clouds in Sentinel-2 satellite imagery and share pointers about models you can try for yourself.
STAC Labeling Competition — Video Recap
By Joe Morrison on January 18th, 2021
Hear from labeling competition participants on why this diverse group of people came together, from across the world, to collaborate on a hallmark training dataset compliant with the STAC standard.
Managing Data Labeling for Machine Learning Projects
By Niki LaGrone on December 2nd, 2020
Responsible for the data labeling for a machine learning project? Here are some insights we’ve developed while managing data labeling for machine learning.
Three Lessons from Running an Imagery Labeling Competition
By Joe Morrison on October 9th, 2020
We joined with Radiant Earth at the Cloud Native Geospatial Sprint to run a labeling competition for non-technical folks. This resulted in over 2.3 million square kilometers mapped and lots of lessons learned.
The Business Case for Raster Vision
By Joe Morrison on August 10th, 2020
Raster Vision is the interface between the fields of earth observation and deep learning, making it easier to apply novel computer vision techniques to geospatial imagery of all types. Joe lays out how it can be implemented in your organization and give you a competitive advantage.