Sunlight and shadow play critical roles in how urban spaces are utilized,
thrive, and grow. While access to sunlight is essential to the success of urban
environments, shadows can provide shaded places to stay during the hot seasons,
mitigate heat island effect, and increase pedestrian comfort levels. Properly
quantifying sunlight access and shadows in large urban environments is key in
tackling some of the important challenges facing cities today. In this paper,
we propose Deep Umbra, a novel computational framework that enables the
quantification of sunlight access and shadows at a global scale. Our framework
is based on a conditional generative adversarial network that considers the
physical form of cities to compute high-resolution spatial information of
accumulated sunlight access for the different seasons of the year. We use data
from seven different cities to train our model, and show, through an extensive
set of experiments, its low overall RMSE (below 0.1) as well as its
extensibility to cities that were not part of the training set. Additionally,
we contribute a set of case studies and a comprehensive dataset with sunlight
access information for more than 100 cities across six continents of the world.
Deep Umbra is available at this https URL

通过 Deep Umbra 这一基于条件生成对抗网络的计算框架，我们能够以全球规模来量化阳光接触与阴影，该框架考虑了城市的物理形态，并以高分辨率的空间信息来计算一年四季的累积阳光接触。我们使用来自七个不同城市的数据来训练模型，并通过一系列广泛实验证明了其低的整体均方根误差（低于 0.1），以及对未参与训练的城市的可扩展性。此外，我们还提供了一系列案例研究和全球六大洲 100 多个城市的阳光接触信息的全面数据集。