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.
An Introduction to Satellite Imagery and Machine Learning
By Joe Morrison on November 5th, 2019
Today, the availability of satellite imagery still far outpaces our capacity to analyze it, but machine learning and tools like Raster Vision are helping.
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.
Transfer Learning from RGB to Multi-band Imagery
By Adeel Hassan on August 30th, 2019
We used transfer learning to teach a model to take advantage of multi-band imagery without discarding the original RGB pre-training. This resulted in significant performance improvement.
Using the Dedupe Machine Learning Library for Cleaning and Matching Data
By Justin Walgran on August 30th, 2019
To deal with issues of apparel facility list data quality and scale quickly and efficiently we need a machine learning tool that can capture the knowledge of domain experts, find commonalities in jumbled text, and confidently compare large lists without the need to compare each individual entry.