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Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations
22 January 2019
Zhoutong Wang
Q. Liang
Fábio Duarte
Fan Zhang
Louis Charron
L. Johnsen
B. Cai
C. Ratti
3DV
HAI
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Papers citing
"Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations"
2 / 2 papers shown
Paved or unpaved? A Deep Learning derived Road Surface Global Dataset from Mapillary Street-View Imagery
Isprs Journal of Photogrammetry and Remote Sensing (ISPRS J. Photogramm. Remote Sens.), 2024
Sukanya Randhawa
Eren Aygun
Guntaj Randhawa
Benjamin Herfort
Sven Lautenbach
Alexander Zipf
244
12
0
24 Oct 2024
Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery
Zhanga Fan
Arianna Salazar Miranda
Fábio Duarte
Lawrence J. Vale
G. Hack
Min Chen
Yu Liu
M. Batty
C. Ratti
283
7
0
02 Jan 2023
1
