Deep Learning for Sidewalk Assessment

Quick Info

Project Date Jan. 1, 2016 - Present
Sponsors: NSF
Keywords: accessible cities, machine learning, street-level accessibility, urban accessibility, deep learning, sidewalks

News

Manaswi Gives Google Tech Talk on Project Sidewalk

Jan 22, 2020 | Jon

Manaswi Saha gave an invited Google Tech Talk on Project Sidewalk and interactive accessibility geo-visualizations. The talk slides are here.

Deep Learning for Sidewalk Accessibility Awarded 'Best Student Paper' at ASSETS'19

Oct 29, 2019 | Jon

Our ASSETS'19 paper "Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery" was just recognized with the 'Best Student Paper Award'--given to only one of the 158 submissions Congrats team!

About

Our Deep Learning Techniques Classify Four Label Types

Examples of the four label types used to train and test our deep learning models for semi-automatic sidewalk assessment: curb ramps, missing curb ramps, obstructions, and surface problems.

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.

Publications

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

keywords: mobility impairments, computer vision, urban informatics, deep learning, sidewalks, neural networks

PDF | doi | Citation | Code • Project Sidewalk • Deep Learning for Sidewalk Assessment

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