Project Sidewalk

Project Description

Roughly 30.6 million individuals in the US have physical disabilities that affect their ambulatory activities; nearly half of those individuals report using an assistive aid such as a wheelchair, cane, crutches, or walker. Despite comprehensive civil rights legislation for Americans with disabilities, many city streets, sidewalks, and businesses remain inaccessible. The problem is not just that street-level accessibility affects where and how people travel in cities but also that there are few, if any, mechanisms to determine accessible areas of a city a priori.

This project describes a two-pronged vision: first, to develop scalable data collection methods for acquiring sidewalk accessibility information using a combination of crowdsourcing, computer vision, and online map imagery, and second, to use this new data to design, develop, and evaluate a novel set of navigation and map tools for accessibility. Our overarching goal is to transform the ways in which accessibility information is collected and visualized for every sidewalk, street, and building façade in America.

Publications

Thumbnail for: Accessibility for Whom? Perceptions of Mobility Barriers Across Disability Groups and Implications for Designing Personalized Maps

Accessibility for Whom? Perceptions of Mobility Barriers Across Disability Groups and Implications for Designing Personalized Maps

Chu Li, Rock Pang, Delphine Labbé, Yochai Eisenberg, Maryam Hosseini, Jon E. Froehlich

Proceedings of CHI 2025 | Acceptance Rate: 24.9% (1249 / 5020)

PDF arXiv doi Code Project Sidewalk Accessibility for Whom?

Thumbnail for: Towards Fine-Grained Sidewalk Accessibility Assessment with Deep Learning: Initial Benchmarks and an Open Dataset

Towards Fine-Grained Sidewalk Accessibility Assessment with Deep Learning: Initial Benchmarks and an Open Dataset

Alex Liu, Kevin Wu, Minchu Kulkarni, Mikey Saugstad, Peyton Rapo, Jeremy Freiburger, Maryam Hosseini, Chu Li, Jon E. Froehlich

Extended Abstract Proceedings of ASSETS 2024 | Acceptance Rate: 66.7% (58 / 87)

PDF doi Deep Learning for Sidewalk Assessment Project Sidewalk

Thumbnail for: “I never realized sidewalks were a big deal”: A Case Study of a Community-Driven Sidewalk Audit Using Project Sidewalk

“I never realized sidewalks were a big deal”: A Case Study of a Community-Driven Sidewalk Audit Using Project Sidewalk

Chu Li, Katrina Ma, Mikey Saugstad, Kie Fujii, Molly Delany, Yochai Eisenberg, Delphine Labbé, Judy L. Shanley, Devon Snyder, Florian P. Thomas, Jon E. Froehlich

Proceedings of CHI 2024 | Acceptance Rate: 26.3% (1060 / 4028)

PDF doi Project Sidewalk + Community Science Project Sidewalk

Thumbnail for: Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe Study

Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe Study

Manaswi Saha, Siddhant Patil, Emily Cho, Evie Yu-Yen Cheng, Chris Horng, Devanshi Chauhan, Rachel Kangas, Richard McGovern, Anthony Li, Jeffrey Heer, Jon E. Froehlich

Proceedings of CHI 2022 | Acceptance Rate: 24.7% (637 / 2579)

PDF doi Talk Project Sidewalk AccessVis Accessibility-Infused Maps Multi-Stakeholder Interviews to Examine Urban Accessibility

Videos

Talks