Descartes Labs to showcase cloud-based machine learning analysis on a global scale
Beginning with satellite imagery and adding historical elements using images and video from NASA and others, the “starting point” Descartes Labs provides is a map ripe for analysis and the tools necessary to generate answers. To name a few use cases, Descartes’ has helped a pool manufacture analyze swimming pools around the world, an air conditioning firm study pollution in China, and farmers and agricultural investors monitor crops.
Descartes, established in 2014 as a spin-off from Los Alamos National Lab, offers a global-scale machine learning platform that powers geographic and temporal analyses of remote sensing data to identify objects, forecast change, and deliver high-performance intelligence solutions.
“We’re ingesting most of the world’s public data sources,” said Smith, Descartes’ head of business development. “The platform and analytics are there to help [our customers], whatever the problem they are trying to solve.”
For example, Descartes Labs was recently awarded a grant from the Defense Advanced Research Projects Agency (DARPA) for a project called “Satellite Imagery Analysis for Automated Global Food Security Forecasting.”
To help perform crop analysis and predict food availability, Descartes overlays historic and current weather patterns on its platform to determine trends and how they affect crops over time. This and other information “puts into context the way a crop is evolving in any specific year to give you an early warning that there may be a shortage,” Smith said. “A bread shortage can lead to civil unrest in some areas.”
Image courtesy of Descartes Labs
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