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Deep Umbra: A Global-Scale Generative Adversarial Approach for Sunlight Access and Shadow Accumulation in Urban Spaces
Researchers: Fabio Miranda, Farah Kamleh, Kazi Shahrukh Omar, Stefan Cobeli
Deep Umbra is 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 shadow cast for the different seasons of the year. Deep Umbra’s primary motivation is the impact that shadow management can have on people’s quality of life, since it can affect levels of comfort, heat distribution, public parks, etc. We also present the Global Shadow Dataset, a comprehensive dataset with the accumulated shadow information for over 100 cities on 6 continents. A webviewer has been developed to visualize accumulated shadow cast over a specific geographic area. The map shows the accumulated shadow for three different days of the year - Summer, Spring/Fall, and Winter. Date: August 16, 2021 - Ongoing |
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