Deep streams of data from Earth-imaging satellites arrive in databases every day, but advanced technology and expertise are required to access and analyze the data. Now a new system, developed in research based at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems. (Photo by NASA via Pxfuel)
More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans of information — including data that reflects climate change, health and poverty — to databases on the ground. There’s just one problem: While the geospatial data could help researchers and policymakers address critical challenges, only those with considerable wealth and expertise can access it.
Now, a team based at UC Berkeley has devised a machine learning system to tap the problem-solving potential of satellite imaging, using low-cost, easy-to-use technology that could bring access and analytical power to researchers and governments worldwide. The study, “A generalizable and accessible approach to machine learning with global satellite imagery,” was published today (Tuesday, July 20) in the journal Nature Communications.