New model reveals pathway for land-efficient U.S. solar expansion

McGill University researchers used deep-learning computer vision to analyze 719 solar projects across the Western U.S. The study establishes a new "land-sparing" benchmark, providing developers with precise data to balance rapid capacity expansion with conservation and land-use priorities.From pv magazine USA As solar is projected to become the world's primary renewable energy source by 2029, land competition has become a bottleneck for utility-scale developers. By applying deep learning neural networks to high-resolution aerial imagery, a McGill University-led team, headed by Associate Professor ...

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