AI for Solar Energy Access: Faster Rooftop Assessments for Wider Adoption
Hundreds of millions of people still live without reliable electricity, and rooftop solar is one of the fastest ways to close that gap. But planning where solar makes sense — across a whole city, district, or region — usually means slow, manual rooftop surveys that many organizations simply can't afford.
Satellite imagery and AI can change that. This webinar looks at how AI and computer vision can analyze satellite images to identify which rooftops are suitable for solar, making energy-access planning faster, cheaper, and easier to scale.
We'll walk through a real-world Omdena project that uses machine learning to assess rooftop characteristics such as usable area, orientation, slope, materials, and obstacles, and discuss how this approach can help organizations plan clean-energy programs for homes, schools, health centers, and underserved communities.
What we'll explore
🔹 How AI and computer vision can analyze satellite imagery at scale
🔹 How AI can assess usable roof area, orientation, slope, materials, and obstacles
🔹 How automated assessments reduce the cost and time of field surveys
🔹 How rooftop analysis can support energy-access and climate-resilience programs
🔹 Challenges in building AI-powered rooftop assessment models, including data gaps
🔹 What it takes to move from a pilot to planning at city or regional scale
Who should attend
NGOs and social enterprises working on energy access
Humanitarian and development organizations
Foundations and donors funding clean-energy and climate programs
Governments, cities, and public agencies planning energy infrastructure
Climate-resilience and sustainability program teams
GIS, remote sensing, and data professionals in the impact sector
Solar and clean-energy organizations working in underserved markets
Join us to see how AI and satellite imagery can help bring clean power to more communities, faster.
This is a pre-recorded webinar with an AI-generated voice.