Deep Umbra: A Generative Approach for Sunlight Access Computation in
  Urban Spaces
Kazi Shahrukh Omar Gustavo Moreira Daniel Hodczak Maryam Hosseini Nicola Colaninno Marcos Lage Fabio Miranda
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.