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

Published onAugust 15, 2019

Researchers have built a tool to use cheap satellite imagery – like Google Maps – to automatically create solar designs with a 91% accuracy rate. 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. The researchers were able to build upon many already existing advances in machine learning to automatically identify structures like buildings and trees, but found most of these tools used LIDAR – a laser-based aerial mapping technology to determine roof geometry, as well as shade from nearby objects. Unfortunately LIDAR data is expensive to collect as drones or airplanes are needed, and not widely available. Google’s Project Sunroof was noted as a high quality LIDAR based tool for this type of work – but is limited to large US cities, and higher populated regions.