  With the effects of global climate change impacting the world, collective
efforts are needed to reduce greenhouse gas emissions. The energy sector is the
single largest contributor to climate change and many efforts are focused on
reducing dependence on carbon-emitting power plants and moving to renewable
energy sources, such as solar power. A comprehensive database of the location
of solar panels is important to assist analysts and policymakers in defining
strategies for further expansion of solar energy. In this paper we focus on
creating a world map of solar panels. We identify locations and total surface
area of solar panels within a given geographic area. We use deep learning
methods for automated detection of solar panel locations and their surface area
using aerial imagery. The framework, which consists of a two-branch model using
an image classifier in tandem with a semantic segmentation model, is trained on
our created dataset of satellite images. Our work provides an efficient and
scalable method for detecting solar panels, achieving an accuracy of 0.96 for
classification and an IoU score of 0.82 for segmentation performance.
