SCL Go

Name: SCL Go
Author: Massachusetts Institute of Technology

Massachusetts Institute of Technology

DOCUMENTED

Published in academic literature

For:Researchers & AcademicsGeneral Public & EnthusiastsIndustry Professionals

Free

Download on the App Store

App Summary

SCL Go is a participatory research application from MIT that enables the public to map urban accessibility by capturing sidewalk imagery with their smartphones for the Sidewalk AI platform. The associated research outlines a method where dedicated algorithms automatically analyze this crowdsourced data to identify features such as sidewalk width, obstacles, and pavement conditions. The authors conclude this approach offers a highly scalable, low-cost strategy for creating city-level accessibility data, fostering community empowerment and more inclusive urban planning.

App Screenshots

Detailed Description

Functionality & Mechanism

SCL Go, developed by the MIT Senseable City Lab, is an augmented reality application for crowdsourced data collection on sidewalk accessibility. The system facilitates rapid mapping by enabling participants to capture smartphone imagery of pedestrian pathways. This visual data is processed by the associated Sidewalk AI platform, which leverages dedicated algorithms to automatically identify and classify key features, including sidewalk width, surface conditions, and the presence of obstacles, thereby generating large-scale, city-level datasets.

Evidence & Research Context

The system's design is detailed in a research article on participatory urban planning, outlining a method to overcome the cost and time limitations of manual sidewalk assessments.
The approach integrates crowdsourced smartphone imagery with Visual AI algorithms to automate the identification of sidewalk accessibility features.
This participatory methodology is presented as a highly scalable strategy for creating comprehensive urban datasets.
The associated research positions this technique as a supplement to, not a replacement for, high-resolution sensing methods.

Intended Use & Scope

This application is intended for urban planners, researchers, and citizen scientists as a data collection tool for evaluating sidewalk accessibility at scale. Its primary utility is generating large datasets to inform urban planning and inclusivity initiatives. The system supplements, but does not replace, official accessibility audits or professional-grade sensing technologies.

Studies & Publications

1 publication

Peer-reviewed research associated with this app.

Development/Design Paper

Mapping sidewalk accessibility with smartphone imagery and Visual AI: a participatory approach

Morra et al. (2024) · Philosophical Transactions of the Royal Society A

Describes the research-driven development of this app

Evaluating sidewalk accessibility is conventionally a manual and time-consuming task that requires specialized personnel. While recent developments in Visual AI have paved the way for automating data analysis, the lack of sidewalk accessibility datasets remains a significant challenge. This study presents the design and validation of Sidewalk AI Scanner, a web app that enables quick, crowdsourced and low-cost sidewalk mapping. The app enables a participatory approach to data collection through imagery captured using smartphone cameras. Subsequently, dedicated algorithms automatically identify sidewalk features such as width, obstacles or pavement conditions. Though not a replacement for high-resolution sensing methods, this method leverages data crowdsourcing as a strategy to produce a highly scalable, city-level dataset of sidewalk accessibility, offering a novel perspective on the city's inclusivity; fostering community empowerment and participatory planning. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.