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.

快速的城市化是当地大气污染的根源，对可持续性和人类健康产生负面影响。为了抵消不利影响，一种新的绿化形式（称为屋顶绿化）正成为一种补救措施。先前的研究和实践已经验证了绿色屋顶对改善大气环境的积极影响，但是大规模的定量研究和相关的城市规划仍然表明，在从大型建筑中获取绿色屋顶的总体面积和空间形态模式方面仍然存在困难。股票。在这项研究中，我们提出了一种基于深度学习的新方法，可以识别可用的屋顶并在可视化中模拟绿色屋顶的大气污染物去除潜力（APRP）。与传统方法相比，我们的方法更加准确和高效。在这项研究中，我们恢复了训练有素的神经网络模型，并获得了令人满意的94.17％的验证准确性。根据我们的结果，石家庄市具有很大的潜力，如果所有选定的屋顶都安装了绿色屋顶，则1.210465×10每年可通过干沉降清除⁶千克/年的污染物。该研究为环境决策和城市绿色系统规划提供了直观的基础。