Rapid urbanization is responsible for local atmospheric pollution, which negatively affects the sustainability and human health. To offset the adverse effects, a new form of greening, termed as the green roof, is becoming one of the remedies. Previous studies and practice have validated the positive impacts of green roofs on atmospheric environment improvement, but the large-scale quantitative studies and the related urban planning still show that difficulties exist in obtaining the overall area and spatial morphological pattern of green roofs from the massive building stock. In this study, we presented a novel method based on deep learning to recognize the rooftops available and simulated the atmospheric pollutants removal potential (APRP) of green roofs in visualization. Compared with traditional methods, our method was more accurate and efficient. In this study, we recovered a trained neural network model and achieved a satisfactory 94.17% validation accuracy. According to our results, Shijiazhuang City offered great potential that if all the selected rooftops installed green roofs, 1.210465×106 kg/year pollutants would be cleaned up with dry deposition. This study provides an intuitive basis for environment policy making and urban green system planning.