We mapped every large solar plant on the planet using satellites and machine learning

NOV 03, 2021

An astonishing 82% decrease in the cost of solar photovoltaic (PV) energy since 2010 has given the world a fighting chance to build a zero-emissions energy system which might be less costly than the fossil-fuelled system it replaces. The International Energy Agency projects that PV solar generating capacity must grow ten-fold by 2040 if we are to meet the dual tasks of alleviating global poverty and constraining warming to well below 2°C.

Critical challenges remain. Solar is “intermittent”, since sunshine varies during the day and across seasons, so energy must be stored for when the sun doesn’t shine. Policy must also be designed to ensure solar energy reaches the furthest corners of the world and places where it is most needed. And there will be inevitable trade-offs between solar energy and other uses for the same land, including conservation and biodiversity, agriculture and food systems, and community and indigenous uses.

Colleagues and I have now published in the journal Nature the first global inventory of large solar energy generating facilities. “Large” in this case refers to facilities that generate at least 10 kilowatts when the sun is at its peak. (A typical small residential rooftop installation has a capacity of around 5 kilowatts).

We built a machine learning system to detect these facilities in satellite imagery and then deployed the system on over 550 terabytes of imagery using several human lifetimes of computing.