Project Sidewalk

projectsidewalk/rampnet-dataset · Datasets at Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Thanks to @hf.co for hosting our large dataset: huggingface.co/datasets/pro...

We're now working on incorporating RampNet into Project Sidewalk. Let's go! 🚀 Shoutout to the whole team, including John, @jaredhwang.bsky.social, Zeyu Wang, and @ismikey.bsky.social. CC @uwcse.bsky.social

#Accessibility #AIforGood #OpenSource #ComputerVision #OpenData

GitHub - ProjectSidewalk/RampNet: RampNet is a two-stage pipeline that addresses the scarcity of curb ramp detection datasets by using government location data to automatically generate over 210,000 a... RampNet is a two-stage pipeline that addresses the scarcity of curb ramp detection datasets by using government location data to automatically generate over 210,000 annotated Google Street View pan...

Try it yourself! See our GitHub for the code, dataset, and paper: github.com/ProjectSidew...

📈 State-of-the-Art Model (0.924 AP)
📚 The largest-ever curb ramp dataset (849,895 labels!)
💻 Everything is 100% open source.

Curb ramps are critical for mobility—for wheelchair users, parents with strollers, travelers with luggage. But detecting them automatically in images is an open problem. We've been working on automatic curb ramp detection for over 10 years, and RampNet significantly outperforms our prior work!

The two-stage RampNet banner

We're launching RampNet, an open-source AI that helps detect curb ramps with near-human accuracy.

The most amazing part? The entire project was conceived of and led by high school student John O'Meara.

🧵 A thread on what we built and why it matters.

A screenshot of Project Sidewalk in India showing a narrow street with colorful buildings

In collaboration with Professor Anupam Sobti and students, we are excited to announce a new pilot deployment in India! 🎉This work furthers our research in mapping and assessing sidewalk accessibility across socio-cultural and geographic contexts: makeabilitylab.cs.washington.edu/media/public...

Accessibility for Whom? We present a large-scale online survey exploring how five mobility groups—users of canes, walkers, mobility scooters, manual wheelchairs, and motorized wheelchairs—perceive sidewalk barriers and diffe...

One map fits all? 🤔 Unfortunately, no! Our #CHI2025 study reveals significant differences in how cane, walker, scooter & wheelchair users perceive sidewalk barriers. Dataset & paper: makeabilitylab.cs.washington.edu/project/acce... This is work led by the amazing @chimichurrichu.bsky.social

📢 HUGE NEWS! Project Sidewalk just crossed 1.1 MILLION manual sidewalk accessibility annotations across 21,000+ km of streets! 🎉 (1,163,413 labels to be exact!). To celebrate and help others build on our work, we have posted a sidewalk feature/barrier dataset to @hf.co huggingface.co/projectsidew...

Project Sidewalk Your labels are used to improve city planning, build accessibility-aware mapping tools, and train machine learning algorithms to automatically find accessibility issues.

to use this unprecedented data to design, develop, and evaluate next-generation mapping tools.

We have mapped sidewalk accessibility in 28 cities across 8 countries including the US, Canada, Mexico, Ecuador, Netherlands, & New Zealand covering nearly 20,000km. Join us: projectsidewalk.org

Hello! 👋 We are an open-source research project run by the @makeabilitylab.bsky.social. We have a two-pronged vision: first, to develop scalable data collection methods for sidewalk accessibility data using crowdsourcing, computer vision, and online map imagery; and second,

Flyer soliciting research participants. It says: "Call for study participants. Criteria: 18 years or older, lives in the US, eligible to use disability parking. Study details: ~1 hour Zoom interview, $25 per hour compensation. Help us improve disability parking by sharing your experiences! Interested? Fill out this screening form tinyurl.com/uw-parking-screener. For questions, email jaredhwa@cs.uw.edu." At the bottom of the flyer are the logos for the Paul G. Allen School of Computer Science & Engineering, styled as a large W with text to the side, and the Makeability Lab, styled as a large M with the text Makeability Lab underneath.

⚠️ We're recruiting participants for a research study! We'd like to understand how people use disability parking. Fill out this screener if you fit the criteria, and we may reach out to interview you.

🔗 tinyurl.com/uw-parking-screener
💸 $25/hr compensation