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.
By Joe Morrison on February 2nd, 2020
Cities may not know it, but by nature of collecting and maintaining various datasets, they are sitting on a treasure trove of machine learning-ready training data.
By Joe Morrison on January 2nd, 2020
Every project with satellite imagery begins with a data source. Here's a rundown of the best options, considering cost, usability, and resolution.
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.
By Joe Morrison on November 22nd, 2019
We're investing heavily in the STAC specification - including building a STAC-compatible Python library and server as well contributing to the Label Extension. We're hoping this work will help accelerate adoption across the geospatial engineering community more broadly.