Quick Info
Project Date
Jan. 1, 2016 - Present
Sponsors:
NSF
Keywords:
accessible cities,
machine learning,
street-level accessibility,
urban accessibility,
deep learning,
sidewalks
News

Jan 22, 2020 | Jon
Manaswi Saha gave an invited Google Tech Talk on Project Sidewalk and interactive accessibility geo-visualizations. The talk slides are here.
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!
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
Jan. 22, 2020 | Google Tech Talk
Seattle, WA
PDF | PPTX | Project Sidewalk | Deep Learning for Sidewalk Assessment | AccessVis | Accessibility-Infused Maps | Urban Accessibility Evolution | Transportation Analytics
Oct. 29, 2019 | ASSETS'19
Pittsburgh, PA