no code implementations • 14 Sep 2020 • Yue Liu, Alex Colburn, Mehlika Inanici
The proposed DNN model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 minutes training time: a) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, b) one-month hourly imagery generated or collected continuously during daylight hours around the equinoxes (8% of the year); or c) 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.
