使用 SegFormer 进行窗墙比检测
Window to Wall Ratio Detection using SegFormer
AI总结：
本文利用 SegFormer 模型通过语义分割技术从街景图像中预测建筑窗墙比，解决了传统建筑性能模拟中数据缺失的问题，展示了计算机视觉在建筑应用中的潜力。
摘要：
窗墙比（WWR）是评估建筑能源、采光和通风性能的关键。研究表明，窗户面积对建筑性能和模拟有很大影响。然而，建立这些环境模型和模拟的数据通常不可用。相反，通常假设所有建筑的标准 WWR 为 40%。本文利用现有的计算机视觉窗户检测方法，通过语义分割从外部街景图像中预测建筑的 WWR，展示了将成熟的计算机视觉技术应用于建筑领域的潜力。
英文摘要：
Window to Wall Ratios (WWR) are key to assessing the energy, daylight and ventilation performance of buildings. Studies have shown that window area has a large impact on building performance and simulation. However, data to set up these environmental models and simulations is typically not available. Instead, a standard 40% WWR is typically assumed for all buildings. This paper leverages existing computer vision window detection methods to predict WWR of buildings from external street view images using semantic segmentation, demonstrating the potential for adapting established computer vision technique in architectural applications
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
