Bringing Open Data to the Apparel Industry
By Deb Boyer on August 31st, 2020
We chatted with the Open Apparel Registry team about how open data and open source are becoming a larger part of the apparel industry.
How We Built a National Hospital Dataset for Coronavirus Relief
By Simon Kassel on June 9th, 2020
We pulled data from disparate hospital data sources to create a comprehensive national dataset of the hospital system for the COVID-19 response, using geocoding, proximity matching, and fuzzy string matching.
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.
STAC: Creating an Ecosystem of SpatioTemporal Assets
By Niki LaGrone, James Santucci and Aaron Su on October 1st, 2019
STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.
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.
Connecting Data and People with the Open Apparel Registry
By Deb Boyer on August 7th, 2019
Since its launch on March 28, the Open Apparel Registry (OAR) has grown to include over 18,300 facilities in 92 countries. We’ve already heard of a few fascinating use cases where data from the OAR contributed to decision making by brands and facilities.