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

20 citations · 21 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2021

Favelas 4D: Scalable methods for morphology analysis of informal settlements using terrestrial laser scanning data

Arianna Salazar Miranda, Guangyu Du, Claire Gorman +3

One billion people live in informal settlements worldwide. The complex and multilayered spaces that characterize this unplanned form of urbanization pose a challenge to traditional…

cs.CV20211 cited

Robust Place Recognition using an Imaging Lidar

Tixiao Shan, Brendan Englot, Fabio Duarte +2

We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds. Utilizing the intensity reading…

cs.SI2021

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Mohammadhossein Ghahramani, Nadina J. Galle, Fabio Duarte +2

Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of…

cs.CV2019

Deep Learning Based Video System for Accurate and Real-Time Parking Measurement

Bill Yang Cai, Ricardo Alvarez, Michelle Sit +2

Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots. Accurate quantification would…

cs.CY201920 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…