Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces
This addresses urban planning challenges by providing scalable sunlight computation for cities worldwide, though it is incremental as it applies existing generative methods to a specific domain.
The paper tackles the problem of quantifying sunlight access and shadows in urban environments at a global scale, proposing Deep Umbra, a generative framework that achieves low RMSE (below 0.1) and is extensible to unseen cities.
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 https://urbantk.org/shadows.