Read about our journey to hiring an outsourced data labeling firm and how we’ve found a partner in CloudFactory.
Read about our journey to hiring an outsourced data labeling firm and how weโve found a partner in CloudFactory.
STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.
We trained remote computer vision workers to provide data labeling for machine learning projects. Here’s what we learned.
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.
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.
Temperate will soon incorporate rural areas into its climate planning capabilities.
There are many books that have influenced me as a software engineer. From the computer science textbooks I used in college, to the animal covered books from OโReilly I used to pick up new languages and tools, to the Clean Code books from Uncle Bob Martin that helped shape the way I approach software development. However,…
Since its launch on March 28, the Open Apparel Registry (OAR) has grown to include over 18,300 facilities in 92 countries. Weโve already heard of a few fascinating use cases where data from the OAR contributed to decision making by brands and facilities.
How do noisy labels affect the accuracy of a deep learning model? We added different amounts of noise to the SpaceNet Vegas buildings dataset and trained some models to find out.
How accurate of a machine learning model can you make? It depends on how we decide to define “accurate”.