Quick Info
Project Date
Jan. 1, 2016 - Present
Sponsors:
NSF
Keywords:
accessible cities,
machine learning,
street-level accessibility,
urban accessibility,
deep learning,
sidewalks
News
Congratulations to Maryam Hosseini on their successful PhD defense on semi-automatic sidewalk assessments. Wonderful work. Terrific presentation. Beautiful slides. Our work together played but a small role in this incredibly impactful research: Hosseini, M., Saugstad, M., Miranda, F., Sevtsuk, A., Silva, C. T., Froehlich, J. E. (2022). Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities. CVPR2020 Workshop: Accessibility, Vision, and Autonomy (AVA). DOI: https://doi.org/10.48550/arXiv.2206.13677 Congratulations Maryam!
Together with Professor Fabior Miranda from the University of Illinois, Chicago and Maryam Hosseini from Rutgers/NYU, we ran a mini-symposium session on the The Future of Global-Scale Spatial Data Collection and Analyses on Urban (in)Accessibility for People with Disabilities at the 2nd Spatial Data Science Symposium 2021. In our session, we brought together experts in disability, human mobility, urban planning, and computer science to discuss state-of-the-art methods for measuring the quality, condition, and accessibility of urban infrastructure, how these methods may enable new types of geospatial analysis and visualization, and the possibilities for data-driven policy change and accessible urban development. Our overarching goal was to identify open challenges, share current work across disciplines, and spur new collaborations. We had over 50 participants join and a set of lightning talks from Anat Caspi and Nick Bolten from UW, Roberto M. Cesar Jr. and Eric K. Tokuda, from the University of São Paulo, Holger Dieterich and Sebastian Felix Zappe from Sozialhelden, Victor Pineda from the Inclusive Cities Lab and Worldenabled.org, Yochai Eisenberg from the University of Illinois, Chicago, and Andres Sevtsuk from UW. Froehlich, J. E., Miranda, F., Hosseini, M., Bolten, N., Caspi, A., Cesar Jr., R. M., Dieterich, H., Eisenberg, Y., Pineda, V., Saha, M., Saugstad, M., Sevtsuk, A., Silva, C. T., Tokuda, E. K., Zappe, S. F. (2021). The Future of Global-Scale Spatial Data Collection and Analyses on Urban (in)Accessibility for People with Disabilities. Spatial Data Science Symposium 2021.

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!

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
Scaling Crowd+AI Sidewalk Accessibility Assessments: Initial Experiments Examining Label Quality and Cross-city Training on Performance
Poster Proceedings of ASSETS'22 | Acceptance Rate: 58.9% (43 / 73)
PDF | doi | Citation | Project Sidewalk • Deep Learning for Sidewalk Assessment
The Future of Urban Accessibility for People with Disabilities: Data Collection, Analytics, Policy, and Tools
Extended Abstract ASSETS'22 Workshop on The Future of Urban Accessibility
PDF | doi | Citation | Project Sidewalk • Urban Accessibility Evolution • Deep Learning for Sidewalk Assessment • Accessibility-Infused Maps
Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities
CVPR2022 Workshop: Accessibility, Vision, and Autonomy (AVA)
PDF | doi | Citation | Project Sidewalk • Deep Learning for Sidewalk Assessment
The Future of Global-Scale Spatial Data Collection and Analyses on Urban (in)Accessibility for People with Disabilities
Extended Abstract Spatial Data Science Symposium 2021
PDF | Citation | Project Sidewalk • Urban Accessibility Evolution • Deep Learning for Sidewalk Assessment • AccessVis • Accessibility-Infused Maps • Sidewalk Gallery
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
Project Members

Michael Duan
Aug 2021 - Present
Undergrad
Computer Science
University of Washington
Project Sidewalk | Deep Learning for Sidewalk Assessment | Sidewalk Gallery

Sho Kiami
Aug 2021 - Present
Undergrad
Computer Science
University of Washington
Project Sidewalk | Deep Learning for Sidewalk Assessment

Logan Milandin
Nov 2021 - Present
Undergrad
Computer Science
University of Washington
Project Sidewalk | Deep Learning for Sidewalk Assessment