The Future of Urban Accessibility for People with Disabilities: Data Collection, Analytics, Policy, and Tools

Froehlich, Jon E.; Eisenberg, Yochai; Hosseini, Maryam; Miranda, Fábio; Adams, Marc A.; Caspi, Anat; Dieterich, Holger; Feldner, Heather A.; González, Aldo Ariel Ocampo; Gyves, Claudina De; Hammel, Joy; Kirkham, Reuben; Kneisel, Melanie; Labbé, Delphine; Mooney, Steve; Pineda, Víctor; PinhÃO, ClÁUdia; RodrÍGuez, Ana; Saha, Manaswi; Saugstad, Michael; Shanley, Judy; Sharif, Ather; Shen, Qing; Silva, Claudio; Sukel, Maarten; Tokuda, Eric K.; Zappe, Sebastian; Zivarts, Anna

doi:10.1145/3517428.3550402

Cited by 5 publications

(2 citation statements)

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“…The first case study then analyzes the impact of building density on winter sunlight access in several parks in the Vaugirard neighborhood in Paris. We further illustrate the utility of our work by measuring shadow accumulation on street networks, commonly utilized in urban accessibility studies [59], [60], [61]. Figure 9 visualizes the distributions of overshadowed streets (i.e., streets with mean shadow accumulation value above 75%) over three seasons for four cities.…”

Section: Case Studiesmentioning

confidence: 92%

“…Highdensity cities such as Washington DC, NYC, and Tokyo present the highest RMSE (∼0.08), and Tokyo, in particular, presents the lowest SSIM value. Given the importance of street-level analyses for urban accessibility [59], [60], [61], we further tested Deep Umbra only considering tile pixels 5) show that, when only considering streets, our results show even lower errors (mean RMSE of ∼0.02).…”

Section: Quantitative Evaluationmentioning

confidence: 94%

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Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces

Omar,

Moreira,

Hodczak

et al. 2024

IEEE Trans. Big Data

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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 environment, 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 urbantk.org/shadows.

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Section: Case Studiesmentioning

confidence: 92%