Raster Vision Archives | Page 2 of 9

Images of imagery overtop of grid paper.

Introducing Raster Vision 0.13

By Adeel Hassan on April 26th, 2021

This release presents a major jump in Raster Vision's power and flexibility. The newly added features allow for finer control of the model training as well as greater flexibility in ingesting data.

Screenshot of GroundWork application Campaign feature.

Company

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.

Image displaying cloud detection on satellite imagery in Spain.

Software Engineering

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.

Line chart that is going up with area underneath filled with a satellite image.

Software Engineering

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.

Chart for labeling project overlaid on image of a desk containing keyboard and clipboard.

Company

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.

Clouds overtop of the area labeled as clouds.

Software Engineering

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.