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Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces
Computer Vision and Pattern Recognition · Submitted 27 Feb 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | 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. | background 1.00 | 0.04 | 0.03 | asserted 0.99 |
| s2 | Properly quantifying sunlight access and shadows in large urban environments is key in tackling some of the important challenges facing cities today. | background 0.95 | 0.05 | 0.03 | asserted 1.00 |
| s3 | In this paper, we propose Deep Umbra, a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. | contribution 1.00 | 0.91 | 0.27 | asserted 1.00 |
| s4 | 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. | method 0.99 | 0.87 | 0.10 | asserted 1.00 |
| s5 | 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. | result 0.95 | 0.97 | 0.24 | asserted 1.00 |
| s6 | 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. | contribution 1.00 | 0.95 | 0.11 | asserted 1.00 |
| s7 | Deep Umbra is available at https://urbantk.org/shadows. | contribution 0.95 | 0.39 | 0.03 | asserted 0.99 |
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Original abstract
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.
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In order to predict the shadow tiles, we leverage the conditional GAN architecture [ref] to synthesize tiles with accumulated shadow.
Conditional Generative Adversarial Nets · Conditional GAN model · 1.00
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