Introducing Raster Vision 0.12
By Lewis Fishgold on July 9th, 2020
We refactored the Raster Vision codebase from the ground up to make it simpler, more consistent, and more flexible. Check out Raster Vision 0.12.
Labeling Satellite Imagery for Machine Learning
By Niki LaGrone on March 24th, 2020
Definitions and visuals of the most common ways to label satellite imagery for machine learning. Includes tips for managing annotations for each type.
Join the OpenCities AI Challenge and Detect Building Footprints from Aerial Imagery
By Simon Kassel on January 16th, 2020
We partnered with Driven Data and the World Bank to develop the Open Cities AI Challenge. This challenge uses machine learning to extract building footprints in unmapped areas to promote disaster resilience.
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?
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?
Betting Big on the Spatiotemporal Asset Catalog (STAC) Standard
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.