Project Description

Recent work has applied machine learning methods to automatically find and/or assess pedestrian infrastructure in online map imagery (e.g., satellite photos, streetscape panoramas). While promising, these methods have been limited by two interrelated issues: small training sets and the choice of machine learning model. In this paper, aided by the recently released Project Sidewalk dataset of 300,000+ image-based sidewalk accessibility labels, we present the frst examination of deep learning to automatically assess sidewalks in Google Street View (GSV) panoramas. Specifcally, we investigate two application areas: automatically validating crowdsourced labels and automatically labeling sidewalk accessibility issues. For both tasks, we introduce and use a residual neural network (ResNet) modifed to support both image and non-image (contextual) features (e.g., geography). We present an analysis of performance, the effect of our non-image features and training set size, and cross-city generalizability. Our results significantly improve on prior automated methods and, in some cases, meet or exceed human labeling performance.


Scaling Crowd+AI Sidewalk Accessibility Assessments: Initial Experiments Examining Label Quality and Cross-city Training on Performance

Michael Duan, Sho Kiami, Logan Milandin, Johnson Kuang, Mikey Saugstad, Maryam Hosseini, Jon E. Froehlich

Poster Proceedings of ASSETS'22 | Acceptance Rate: 58.9% (43 / 73)

Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities

Maryam Hosseini, Mikey Saugstad, Fabio Miranda, Andres Sevtsuk, Cláudio T. Silva, Jon E. Froehlich

CVPR2022 Workshop: Accessibility, Vision, and Autonomy (AVA)

Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery

Galen Weld, Esther Jang, Anthony Li, Aileen Zeng, Kurtis Heimerl, Jon E. Froehlich

Proceedings of ASSETS 2019 | Acceptance Rate: 25.9% (41 / 158) | Best Paper Award