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Rooftop solar photovoltaic (PV) systems are playing an increasingly important role in the worldwide shift toward sustainable energy. This development creates a need for precise, high-resolution geospatial assessments of rooftop PV’s technical potential for policy formulation and power system planning. To tackle this issue, we introduce a comprehensive framework that integrates various satellite imagery sources with deep learning techniques to estimate rooftop PV electricity generation. The framework employs a deep learning-based inversion to estimate hourly solar radiation from geostationary satellite imagery. It uses an automated segmentation model to identify building footprints from high-resolution satellite imagery. This enables a detailed assessment of available rooftop areas and hourly simulation of power output at a 100 m spatial resolution. A case study conducted in Jiangsu Province shows that the framework works effectively over large regions and can be scaled from specific sites to broader areas across different time scales. Results indicate that the province’s rooftop resources offer a potential installed capacity of 245.17 GW, which could generate 290.66 TWh of electricity annually. These findings underscore the substantial opportunity for reducing carbon emissions by expanding rooftop PV deployment.