Machine learning on satellite imagery is revolutionizing disaster relief. What does ethical machine learning mean in this field?
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.
Today, the availability of satellite imagery still far outpaces our capacity to analyze it, but machine learning and tools like Raster Vision are helping.
Read about our journey to hiring an outsourced data labeling firm and how we’ve 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.
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”.