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.
Using Noisy Labels to Train Deep Learning Models on Satellite Imagery
By Lewis Fishgold on August 5th, 2019
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.
An Introduction to Machine Learning Accuracy Metrics
By Joe Morrison on July 31st, 2019
How accurate of a machine learning model can you make? It depends on how we decide to define "accurate".
Introducing WMS and WCS Support for GeoTrellis and Raster Foundry
By Nathan Zimmerman on July 3rd, 2019
Raster Foundry and GeoTrellis now support WMS and WCS standards, enabling streamlined workflows that utilize multiple data sources.