We examine the feasibility of using deep learning to infer 33 classes of sidewalk accessibility conditions in pre-cropped streetscape images, including bumpy, brick/cobblestone, cracks, height difference (uplifts), narrow, uneven/slanted, pole, and sign. We present two experiments: first, a comparison between two state-of-the-art computer vision models, Meta’s DINOv2 and OpenAI’s CLIP-ViT, on a cleaned dataset of ∼ 24k images; second, an examination of a larger but noisier crowdsourced dataset (∼ 87k images) on the best performing model from Experiment 1. Though preliminary, Experiment 1 shows that certain sidewalk conditions can be identified with high precision and recall, such as missing tactile warnings on curb ramps and grass grown on sidewalks, while Experiment 2 demonstrates that larger but noisier training data can have a detrimental effect on performance. We contribute an open dataset and classification benchmarks to advance this important area.

Despite decades of effort, pedestrian infrastructure in cities continues to be unsafe or inaccessible to people with disabilities. In this paper, we examine the potential of community-driven digital civics to assess sidewalk accessibility through a deployment study of an open-source crowdsourcing tool called Project Sidewalk. We explore Project Sidewalk’s potential as a platform for civic learning and service. Specifically, we assess its effectiveness as a tool for community members to learn about human mobility, urban planning, and accessibility advocacy. Our findings demonstrate that community-driven digital civics can support accessibility advocacy and education, raise community awareness, and drive pro-social behavioral change. We also outline key considerations for deploying digital civic tools in future community-led accessibility initiatives.

Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId’s success factors, limitations, and its generalizability to other crowdsourced science domains.

Public transportation provides vital connectivity to people with disabilities, facilitating access to work, education, and health services. While modern navigation applications provide a suite of information about transit options—including real-time updates about bus or train arrivals—they lack data about the accessibility of the transit stops themselves. Bus stop features such as seatings, shelters, and landing areas are critical, but few cities provide this information. In this demo paper, we introduce BusStopCV, a Human+AI web prototype for scalably collecting data on bus stop features using real-time computer vision and human labeling. We describe BusStopCV’s design, custom training with the YOLOv8 model, and an evaluation of 100 randomly selected bus stops in Seattle, WA. Our findings demonstrate the potential of BusStopCV and highlight opportunities for future work.

Increasingly, crowds plus machine learning techniques are being used to semi-automatically analyze the accessibility of built environments; however, open questions remain about how to effectively combine the two. We present two experiments examining the effect of crowdsourced data in automatically classifying sidewalk accessibility features in streetscape images. In Experiment 1, we investigate the effect of validated data—which has been voted correct by the crowd but is more expensive to collect—compared with a larger but noisier aggregate dataset. In Experiment 2, we examine whether crowdsourced labeled data gathered in one city can be used as effective training data for another. Together, these experiments contribute to the growing literature in Crowd+AI approaches for semi-automatic sidewalk assessment and help identify pertinent challenges.

Inaccessible urban infrastructure creates and reinforces systemic exclusion of people with disabilities and impacts public health, physical activity, and quality of life for all. To improve the design of our cities and to enable more equitable policies and location-centric technology designs, we need new data collection techniques, data standards, and accessibility-infused analytic tools and interactive maps focused on the quality, safety, and accessibility of pathways, transit ecosystems, and buildings. In this workshop, we bring together leading experts in human mobility, urban design, disability, and accessible computing to discuss pressing urban access challenges across the world and brainstorm solutions. We invite contributions from practitioners, transit officials, disability advocates, and researchers.

What do sidewalk accessibility problems look like? How might these problems differ across cities? In this poster paper, we introduce Sidewalk Gallery, an interactive, filterable gallery of over 500,000 crowdsourced sidewalk accessibility images across seven cities in two countries (US and Mexico). Gallery allows users to explore and interactively filter sidewalk images based on five primary accessibility problem types, 35 tag categories, and a 5-point severity scale. When browsing images, users can also provide feedback about data correctness. We envision Gallery as a tool for teaching in urban design and accessibility and as a visualization aid for disability advocacy.

How do sidewalks change over time? Are there geographic or socioeconomic patterns to this change? These questions are important but difficult to address with current GIS tools and techniques. In this demo paper, we introduce three preliminary crowd+AI (Artificial Intelligence) prototypes to track changes in street intersection accessibility over time—specifically, curb ramps—and report on results from a pilot usability study.

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement these findings, we report on an interview study with three key stakeholder groups (N=14) soliciting reactions to our tool and methods. Our findings demonstrate the potential of virtually auditing urban accessibility and highlight tradeoffs between scalability and quality compared to traditional approaches.

We present Project Sidewalk, a new online tool that allows anyone-from motivated citizens to government workers-to remotely label accessibility problems by virtually walking through city streets. Basic game design principles such as interactive onboarding, mission-based tasks, and stats dashboards are used to train, engage, and sustain users. We describe the current Project Sidewalk system, present results of a pilot public deployment with 581 users, and discuss open questions and future work.