用深度学习自动识别建筑立面光伏安装潜力，助力城市低碳规划
Facade Segmentation for Solar Photovoltaic Suitability
- 结合建筑立面结构信息，用SegFormer-B5模型识别可装光伏的区域
- 在373个立面数据上验证，实际可安装潜力远低于理论值
- 适合城市能源规划、智能建筑设计与光伏部署决策者
建筑一体化光伏（BIPV）立面是城市减碳的重要路径，尤其在屋顶面积不足或地面安装受限时。尽管基于机器学习的屋顶光伏规划研究较成熟，但立面自动化方法仍稀缺且过于简化。本文提出一个集成立面建筑结构信息的流程，可自动识别适合光伏安装的表面并估算太阳能潜力。该流程在CMP Facades数据集上微调SegFormer-B5，将语义分割结果转化为立面级光伏适用性掩码及考虑组件尺寸与间距的光伏板布局。应用于来自十个城市的373个已知尺寸立面数据，结果显示实际可安装的BIPV潜力显著低于理论值，为可靠的城市能源规划提供重要参考。随着立面影像数据日益普及，该流程可推广至全球城市光伏规划。
原文摘要 · Abstract (English)
Building integrated photovoltaic (BIPV) facades represent a promising pathway towards urban decarbonization, especially where roof areas are insufficient and ground-mounted arrays are infeasible. Although machine learning-based approaches to support photovoltaic (PV) planning on rooftops are well researched, automated approaches for facades still remain scarce and oversimplified. This paper therefore presents a pipeline that integrates detailed information on the architectural composition of the facade to automatically identify suitable surfaces for PV application and estimate the solar energy potential. The pipeline fine-tunes SegFormer-B5 on the CMP Facades dataset and converts semantic predictions into facade-level PV suitability masks and PV panel layouts considering module sizes and clearances. Applied to a dataset of 373 facades with known dimensions from ten cities, the results show that installable BIPV potential is significantly lower than theoretical potential, thus providing valuable insights for reliable urban energy planning. With the growing availability of facade imagery, the proposed pipeline can be scaled to support BIPV planning in cities worldwide.
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