most citedQuantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations

20 citations · 20 across the 1 of their papers we have counts for

collaborators

1 paper

cs.CY2019★ 20 cited

Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations

Zhoutong Wang, Qianhui Liang, Fabio Duarte +5

Legibility is the extent to which a space can be easily recognized. Evaluating legibility is particularly desirable in indoor spaces, since it has a large impact on human behavior…