用AI分析街景图，自动评估建筑外墙装光伏的潜力。
Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
- 通过视觉与语言大模型结合，修正视角畸变并理解墙面元素
- 对80栋建筑测试，面积估算误差仅6.2%±2.8%，效率提升100倍
- 适合城市能源规划和光伏部署决策者使用
建筑外墙是高密度城市中极具潜力的太阳能发电资源，但因其复杂几何结构和语义成分，光伏（PV）潜力评估仍具挑战。本研究提出SF-SPA（语义外墙光伏评估）框架，将街景图像自动转化为可量化的光伏部署评估结果。该方法融合计算机视觉与人工智能技术，解决三大难题：视角畸变校正、墙面元素语义理解、光伏布局空间推理。四阶段流程包括几何矫正、零样本语义分割、大语言模型（LLM）引导的空间推理及能量仿真。在四个国家的80栋建筑上验证，平均面积估算误差为6.2%±2.8%，与专家标注高度一致。单建筑自动化评估耗时约100秒，较人工方法效率大幅提升。模拟发电量预测验证了方法的可靠性，适用于区域潜力分析、城市能源规划及建筑一体化光伏（BIPV）部署。代码已开源：https://github.com/CodeAXu/Solar-PV-Installation
原文摘要 · Abstract (English)
Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to address three key challenges: perspective distortion correction, semantic understanding of facade elements, and spatial reasoning for PV layout optimization. Our four-stage pipeline processes images through geometric rectification, zero-shot semantic segmentation, Large Language Model (LLM) guided spatial reasoning, and energy simulation. Validation across 80 buildings in four countries demonstrates ro bust performance with mean area estimation errors of 6.2% ± 2.8% compared to expert annotations. The auto mated assessment requires approximately 100 seconds per building, a substantial gain in efficiency over manual methods. Simulated energy yield predictions confirm the method's reliability and applicability for regional poten tial studies, urban energy planning, and building-integrated photovoltaic (BIPV) deployment. Code is available at: https:github.com/CodeAXu/Solar-PV-Installation
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