Using machine learning and cheap satellite data to design rooftop solar power

This author's solar punk novel involves the team from Clean Coalition using their power grid maps, guiding business areas with strategic solar storage placement on a city level, taking into account Tesla's 1,600 superchargers, and everyone having solar storage in their homes. At some percentage, within this super distributed network we will gain resiliency. To get there will take patience, and smart tools. Researchers at the University of Massachusetts, Amherst campus, have built a software tool, called DeepRoof, which they say has achieved a "true positive rate" of 91.1% in identifying a roof's solar power potential, while using widely available (and cheap) satellite data from tools like Google Earth. Their goal in Deep Roof: a Data-Driven Approach For Solar Potential Estimation Using Rooftop Imagery, is to take a list of address (or GPS coordinates) from a contractor and hand back the solar power potential of those sites.