With Descartes Labs' GeoVisual Search, if a user clicks on ships in a Marina, GVS processes satellite and aerial imagery in the cloud to identify similar objects around the world. Users can also use geographic or temporal parameters to confine the GVS search. Credit: Descartes Labs

SAN FRANCISCO — Descartes Labs, a startup that specializes in applying machine learning to geospatial data, announced Aug. 24 that it closed a $30 million Series B funding round led by March Capital, a Los Angeles-based venture firm.

With the financing, Descartes Labs plans to enhance its “data refinery,” the cloud-based supercomputing platform it uses to draw useful information and insight from disparate datasets. A May article in The Economist publicized the idea of data replacing oil as the world’s most precious resource and pointed out that like oil data becomes more profitable when it is refined.

“In addition to enhancing our data refinery, we plan on using most of the financing to grow our company,” Mark Johnson, Descartes Labs chief executive, told SpaceNews by email. “We’re currently at 40 full-time employees and plan to double the team within one to two years.”

Santa Fe, New Mexico-based Descartes Labs spun off from the U.S. Energy Department’s Los Alamos National Laboratory in 2014. Descartes Labs raised $3.3 million in seed funding and $5 million in Series A funding in 2015.

Crosslink Capital and Cultivian Sandbox, the venture firms that led Descartes’ seed and Series A rounds, also provided funding in Series B along with Cargill, the Minneapolis-based agriculture giant and Descartes customer.

Debra Werner is a correspondent for SpaceNews based in San Francisco. Debra earned a bachelor’s degree in communications from the University of California, Berkeley, and a master’s degree in Journalism from Northwestern University. She...