Solar power is an environmentally friendly energy source that is actively promoted by government agencies and power distribution companies. Detecting solar panel installations can help government bodies offer incentives such as tax credits and exemptions to residents who adopt solar energy. Policymakers can also use this data to assess adoption rates, identify underserved areas, and design targeted awareness campaigns to promote solar energy usage. Additionally, installation and utility companies can leverage this information to refine their marketing and outreach strategies.

Traditional methods of identifying solar panel installations—such as surveys and field visits—are time-consuming, labour-intensive, and prone to error. Deep learning models, with their ability to recognize complex patterns in imagery, offer a powerful alternative. This model automates the detection of solar panels, significantly reducing the time and effort required while delivering accurate and scalable results.