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 earlier this month in the journal Nature Communications.
“Satellite images contain an incredible amount of data about the world, but the trick is how to translate the data into usable insights without having a human comb through every single image,” said co-author Esther Rolf, a final-year Ph.D. student in computer science. “We designed our system for accessibility, so that one person should be able to run it on a laptop, without specialized training, to address their local problems.”
“We’re entering a regime in which our actions are having truly global impact,” said co-author Solomon Hsiang, director of the Global Policy Lab at the Goldman School of Public Policy. “Things are moving faster than they’ve ever moved in the past. We’re changing resource allocations faster than ever. We’re transforming the planet. That requires a more responsive management system that is able to see these things happen, so that we can respond in a timely, effective way.”
The project was a collaboration between the Global Policy Lab, which Hsiang directs, and Benjamin Recht’s research team in the department of Electrical Engineering and Computer Sciences. Other co-authors include Rausser College alumni Tamma Carleton (PhD '18, Agricultural & Resource Economics), now at University of California, Santa Barbara; Jonathan Proctor (PhD '19, ARE), now at Harvard’s Center for the Environment and Data Science Initiative; and Ian Bolliger (Phd '20 Energy & Resources), now at the Rhodium Group. Other Bekeley authors are Vaishaal Shankar, now at Amazon; and Berkeley PhD student Miyabi Ishihara.
All of them were at Berkeley when the project began. Their collaboration has been remarkable for bringing together disciplines that often look at the world in different ways and speak different languages: computer science, environmental and climate science, statistics, economics and public policy.
But they have been guided by a common interest in creating an open access tool that democratizes the power of technology, making it usable even by communities and countries that lack resources and advanced technical skill. “It’s like Ford’s Model T, but with machine learning and satellites,” Hsiang said. “It’s cheap enough that everyone can now access this new technology.”