Annual luminance maps provide meaningful evaluations for occupants’ visual comfort and perception. This paper presents a novel data-driven approach for predicting annual luminance maps from a limited number of point-in-time high-dynamic-range imagery by utilizing a deep neural network. A sensitivity analysis is performed to develop guidelines for determining the minimum and optimum data collection periods for generating accurate maps. The proposed model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 min training time: (i) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, (ii) one-month hourly imagery generated during daylight hours around the equinoxes; or (iii) 9 days of hourly data collected around the spring equinox, summer and winter solstices (2.5% of the year) all suffice to predict the luminance maps for the rest of the year. The DNN predicted high-quality panoramas are validated against Radiance renderings.

年度亮度图可为乘员的视觉舒适度和感知度提供有意义的评估。本文提出了一种新颖的数据驱动方法，可通过利用深度神经网络从有限数量的时间点高动态范围图像中预测年度亮度图。进行敏感性分析以开发用于确定最小和最佳数据收集周期以生成精确地图的准则。所提出的模型可以在30分钟的训练时间内从以下三个选项之一忠实地预测高质量的年度全景亮度图：（i）在白天均匀分布在一年中的5％的时间点亮度图像，（ ii）在昼夜平分点附近在白天生成的一个月的每小时图像；或（iii）在春分前后收集的9天每小时数据，夏至冬至（每年的2.5％）都足以预测该年剩余时间的亮度图。DNN预测的高质量全景图已针对Radiance渲染进行了验证。