Quick Info
Project Date
Jan. 1, 2016 - Present
Sponsors:
NSF
Keywords:
accessible cities,
machine learning,
street-level accessibility,
urban accessibility,
deep learning,
sidewalks
News
Our ASSETS'19 paper "Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery" was just recognized with the 'Best Student Paper Award'--given to only one of the 158 submissions Congrats team!
Oct 28, 2019 | Jon
Both of our ASSETS'19 papers were nominated for 'Best Paper' at ASSETS'19: Weld, G., Jang, E., Li, A., Zeng, A., Heimerl, K., Froehlich, J. E. (2019). Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery. Proceedings of ASSETS 2019. Jain, D., Desjardins, A., Findlater, L., Froehlich, J. E. (2019). Autoethnography of a Hard of Hearing Traveler. Proceedings of ASSETS 2019. Congrats to all authors!
About
Our Deep Learning Techniques Classify Four Label Types
Examples of the four label types used to train and test our deep learning models for semi-automatic sidewalk assessment: curb ramps, missing curb ramps, obstructions, and surface problems.
Publications
Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery
Proceedings of ASSETS 2019
|
Acceptance Rate: 25.9%
(41 / 158)
|
Best Paper Award
PDF | doi | Citation | Code • Project Sidewalk • Deep Learning for Sidewalk Assessment
Talks
Oct. 29, 2019 | ASSETS'19
Pittsburgh, PA
