College - Author 1
College of Engineering
Department - Author 1
Computer Science Department
College - Author 2
College of Engineering
Department - Author 2
Computer Science Department
Advisor
Fahim Khan, College of Engineering, Computer Science and Software Engineering
Funding Source
This research was funded by Cal Poly's Summer Undergraduate Research Program (SURP), organized by the College of Engineering and the CSSE Department
Date
10-2025
Abstract/Summary
This research project will develop and evaluate a smartphone-based, AI-powered system to crowdsource and analyze accessibility features and barriers in public spaces. Using computer vision and geospatial mapping, the system will identify and categorize issues such as uneven sidewalks, missing or inadequate curb ramps, damaged tactile paving, obstructive overhangs, and the absence of visual or auditory wayfinding cues. The overarching goal is to generate a dynamic, real-time accessibility map that empowers individuals with diverse mobility, sensory, and cognitive needs to navigate public spaces more safely and confidently. The project will integrate technologies and methods from applied machine learning, mobile computer vision, human-centered interface design, and participatory citizen science. The system will support multiple accessibility dimensions, including needs associated with wheelchair and stroller users, people with vision or hearing impairments, neurodiverse individuals, and older adults with endurance or balance limitations. Building on prior published works such as SmartCS (2024), a platform enabling codeless development of ML-powered apps for citizen science, this project will apply similar frameworks for data collection, annotation, and model deployment in an inclusive, community-driven context. The resulting application will feature an interactive, filterable map interface that visualizes both AI-detected and human-reported barriers, enabling both public users and civic planners to understand and prioritize accessibility needs. The student researcher will work closely with the faculty mentor on training and fine-tuning computer vision models based on deep neural networks, collecting and annotating accessibility-related datasets, prototyping and refining the mobile app, and coordinating a pilot study on the Cal Poly campus and in the City of San Luis Obispo (if time permits). Through this experience, the student will gain hands-on skills in inclusive technology development, interdisciplinary research, and ethical community engagement.
October 1, 2025.
URL: https://digitalcommons.calpoly.edu/ceng_surp/103