Exploratory Advanced 

Research Program Sidewalk Mapping for 

Pedestrian Navigation

Workshop Summary Report
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Cover photo credits left to right:

© 2022 Marshall & Coppola. 

© 2022 Marshall & Coppola. 

© 2022 City of Austin. 
1. Report No.

FHWA-HRT-23-084

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Sidewalk Mapping for Pedestrian Navigation Workshop 

Summary Report

5. Report Date 

September 2023

6. Performing Organization Code

7. Author(s)

Nate Deshmukh-Towery, Jennifer Little, Charles Mills, 

Samuel Waitt

8. Performing Organization 

Report No.

9. Performing Organization Name and Address

Volpe National Transportation Systems Center 

55 Broadway, Kendall Square

Cambridge, MA 02142

Schatz Publishing Group

11950 W. Highland Ave.

Blackwell, OK 74631

10. Work Unit No.

11. Contract or Grant No.

12. Sponsoring Agency Name and Address

Office of Corporate Research, Technology, and Innovation 

Management

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and 

Period Covered

Workshop Summary Report

October 19 and 20, 2022

14. Sponsoring Agency Code

HRTM-30

15. Supplementary Notes

16. Abstract

On October 19 and 20, 2022, the Federal Highway Administration’s Exploratory Advanced Research 

Program held a workshop on sidewalk mapping to complement the findings of the program’s 

“Mapping and Navigation for Pedestrians with Disabilities” white paper. This paper, included in this 

summary report’s appendix, identified the need to define the role of government in this research area.

It also recommended an investigation of replicable data collection methods. The workshop focused 

on research needs for sidewalk mapping as well as the roles of various stakeholders who can 

impact the deployment of sidewalk mapping technologies.

17. Key Words

Wayfinding, Americans with Disabilities Act (ADA), 

ADA transition plan, sidewalk mapping

18. Distribution Statement

No restrictions. This document is available 

to the public through the National Technical 

Information Service, Springfield, VA 22161.

https://www.ntis.gov

19. Security Classif. 

(of this report)

Unclassified

20. Security Classif. 

(of this page)

Unclassified

21. No. of Pages

14

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.

Technical Report Documentation Page

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  I
 

*SI is the symbol for international System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(revised March 2003)

II  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
Contents

Executive Summary 1

Workshop Background 1

Key Takeaways 1

Presentations 2

Presentation 1—Where the Sidewalk Begins: Quantifying 

Sidewalks via Remote Sensing (Dr. Wesley Marshall, 

University of Colorado Denver) 2

Presentation 1 Question and Answer (Q&A) Session 2

Presentation 2—Crowd+AI Tools to Map, Analyze, and 

Visualize Sidewalk Accessibility (Dr. Yochai Eisenberg, University 

of Illinois Chicago, and Chu Li, University of Washington) 3

Presentation 2 Q&A Session 4

Presentation 3—City of Austin Sidewalks and geographic 

information system (GIS) (John Eastman, City of Austin, TX) 4

Presentation 3 Q&A Session 5

General Workshop Discussion Q&A 6 

Question 1: What Do Researchers Need from a 

Partner to Pilot a Technology? 6

Question 2: From a Public Agency Perspective, 

What Is the Motivation for Working with a 

Research Team on Testing New Technology? 6

Question 3: How Can Smaller Cities and 

Rural Areas Effectively Deploy These Technologies? 6

Question 4: What Is the Private Sector’s 

Role in Deploying These Technologies? 6

Appendix. “Mapping and Navigation for Pedestrians 

with Disabilities” White Paper 7

Introduction 7

Navigation for Pedestrians with Disabilities Overview 7

Intended Audiences 8

Understanding User Needs 8

Landscape of Current Data Collection Practices and Approaches 9

Data Collection Methods 9

Type of Data Collected 9

Review of Specific Data Collection Entries and/or Efforts 10

OpenSidewalks (System in Development) 10

AccessMap (Available in Seattle, WA; Bellingham, WA;

and Mt. Vernon, WA) 1 1

EsriTM (System is a Commercial Product) 1 1

SideGuide (System in Development) 1 1

Recommendations 12

References 14

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  III
LIST OF FIGURES

Figure 1. Image. Identifying sidewalks within planimetric data. 2

Figure 2. Image. Sidewalk width captured from LiDAR point cloud data. 2

Figure 3. Image. The Project Sidewalk crowdsourcing workflow showing potential map 

imagery input datasets, the crowdsourcing workflow including labeling and 

validation missions, and how these human labels are used to train machine 

learning algorithms. 3

Figure 4. Image. Project Sidewalk user interface for labeling 

sidewalk accessibility using streetscape imagery. 3

Figure 5. Image. City of Austin, TX, sidewalk condition data collection. 4

Figure 6. Image. City of Austin, TX, citywide sidewalk 

prioritization and condition assessment maps (2022). 5

Figure 7. Screenshot. AccessMap—the interface to Seattle’s sidewalks. 11

LIST OF TABLES

Table 1. City of Seattle/OpenSidewalks data collection. 10

IV  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
WORKSHOP BACKGROUND

On October 19 and 20, 2022, the Federal 

Highway Administration’s (FHWA) 

Exploratory Advanced Research (EAR) 

Program held the “Mapping and Navigation 

for Pedestrians with Disabilities” workshop 

to complement the findings of the program’s 

“Mapping and Navigation for Pedestrians 

with Disabilities” white paper. The workshop 

focused on research needs for sidewalk 

mapping as well as the roles of various 

stakeholders who can impact the deployment 

of sidewalk mapping technologies.(1) The white 

paper, included in this summary report’s 

appendix, identified the need to define the 

role of government in this research area, 

and it also recommended an investigation 

of replicable data collection methods.

The workshop featured the following 

presentations:(1)

• Dr. Wesley Marshall 

(University of Colorado Denver).

• Dr. Yochai Eisenberg 

(University of Illinois Chicago) and 

Chu Li (University of Washington).

• John Eastman 

(city of Austin, TX).

KEY TAKEAWAYS

The workshop had the following takeaways:(1)

• Accessible and connected sidewalks are

essential for quality of life. Workshop 

attendees agreed that accessible 

sidewalks are important for improving 

transportation equity and mobility for all, 

including individuals with disabilities and 

older adults. Moreover, the connectivity 

of sidewalks is a necessary component to 

consider in sidewalk mapping activities.

• Standards for condition ratings and 

individual data elements related to 

sidewalk infrastructure are critical to 

Executive Summary

data sharing and investment. Participants 

indicated that communities and academics 

often develop their own ratings because 

there is no standard guidance. Standard 

ratings would make it easier to improve 

pedestrian access across jurisdictions and 

provide much-needed direction for 

jurisdictions with limited capacity. No 

standards currently exist that allow 

communities to link data effectively 

across geographies.

• Data collection technologies for sidewalk 

infrastructure vary widely in sophistication 

and maturity. In addition, the potential for 

artificial intelligence (AI) and machine 

learning to enhance these technologies 

exists, particularly in the area of 

crowdsourced data. The advancement of 

these technologies could make this data 

collection faster and more cost efficient.

• Policies governing sidewalk infrastructure 

and data collection would benefit from 

Federal coordination and streamlining. 

Such guidance can provide a path 

forward for communities interested in 

improving sidewalk infrastructure and 

make data collection and sidewalk 

improvement processes more accessible 

to jurisdictions with fewer resources.

• Sidewalk obstruction data are critical 

to understanding sidewalk accessibility, 

but the methods for gathering these 

data are limited. Obstructions such as 

benches, bus shelters, fire hydrants, and 

streetlights can significantly impact the 

useful width of sidewalks and compliance 

with the Americans with Disabilities Act 

(ADA) standards.(2) The development 

of new methods to account for static 

obstructions during data collection and 

analysis would improve the use of 

sidewalk accessibility findings.

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  1
Presentations

PRESENTATION 1—WHERE THE SIDEWALK 

BEGINS: QUANTIFYING SIDEWALKS VIA 

REMOTE SENSING

(DR. WESLEY MARSHALL, 

UNIVERSITY OF COLORADO DENVER)

Dr. Marshall described several related research 

activities focused on measuring fundamental 

aspects of sidewalk availability, including 

sidewalk width.(1) This research focused on light 

detection and ranging (LiDAR) and planimetric 

(the horizontal distance between points) data 

collection techniques.(3) Images from the 

presentation can be seen in figure 1 and figure 2.

Dr. Marshall’s presentation had the following 

major points: (1)

• LiDAR technology can measure sidewalk 

width as well as sidewalk lips and slopes. 

Tools from private firms can collect this 

information, but it can take upward of 

20 min per scan. By contrast, Marshall’s 

team found that LiDAR data collected 

from smartphones are useful for 

gathering width data, and collection 

is much faster. Additional research is 

underway regarding smartphones’ 

accuracy in collecting lip and slope data.

• The team used planimetric data to assess 

sidewalk availability and reached out to 

cities across the United States to inquire 

about existing planimetric data. The analysis 

included data provided from 16 cities.

• Static obstructions are generally not 

accounted for in planimetric data, which 

is a weakness of using this type of data 

to measure sidewalk availability. Static 

obstructions can considerably impact the 

clear width of sidewalks and compliance 

with ADA standards as well as other 

width guidelines.(2)

• A study of sidewalk data from 

Cambridge, MA, found that the number 

of ADA-compliant sidewalks in the city 

is cut nearly in half when accounting 

for static obstructions.

Presentation 1 Question and Answer (Q&A) Session

Question: The smartphone LiDAR looks 

impressive. Can you extract data from it? 

What does the data look like?

The data are point cloud data and were 

collected from a handheld smartphone.

© 2022 Marshall & Coppola. Figure 2. Image. Sidewalk width 

captured from LiDAR point cloud data.(1)

© 2022 Marshall & Coppola. Figure 1. Image. Identifying 

sidewalks within planimetric data.(1)

2  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
PRESENTATION 2—CROWD+AI TOOLS 

TO MAP, ANALYZE, AND VISUALIZE 

SIDEWALK ACCESSIBILITY 

(DR. YOCHAI EISENBERG, UNIVERSITY OF 

ILLINOIS CHICAGO, AND CHU LI, UNIVERSITY 

OF WASHINGTON)

Dr. Eisenberg discussed the role of crowdsourced 

data in addressing a critical gap in sidewalk 

accessibility: Data about sidewalk locations, 

conditions, and obstructions are not collected 

by many cities. Additionally, for these data, there 

are no standardized formats. Images from the 

presentation can be seen in figure 3 and figure 4.

(1)

The presentation included the following 

major points: (1)

• ADA lawsuits, among other factors, could 

influence interest in collecting sidewalk 

data to inform sidewalk investments.

• Gaps in planning limit the responsiveness of 

some governments to ADA requirements.(2)

Dr. Eisenberg and his colleagues found that 

very few communities have an ADA 

transition plan. Among the 401 government 

entities reviewed, only 54 had an ADA 

transition plan readily available. Out of 

these 54 plans, only 7 met all the minimum 

criteria. Communities with a transition 

plan generally had better accessibility 

than those that did not.

• Traditional sidewalk audits are often 

costly and time consuming. Additionally, 

the use of the data is generally restricted, 

limiting analysis and utility for wayfinding 

applications.

• Project Sidewalk addresses common 

sidewalk data collection and analysis 

concerns by using existing online map 

imagery, crowdsourcing condition/

obstruction labeling and validation 

activities, and applying machine-learning

techniques to assess sidewalk conditions.(4)

The project has been deployed in 10 cities 

in North America and 2 in Europe. The

project has collected more than 700,000 

condition labels and 400,000 validations. 

The availability of online map imagery 

limits the project.

• Project Sidewalk data can find trends 

in sidewalk conditions in a particular 

community. Several cities have used the 

data to develop improvement plans.

• The research team plans to conduct 

cross-regional studies regarding sidewalk 

infrastructure, mobility, and equity in the 

future. For example, the research team 

will compare curb issues across cities as 

well as evaluate the correlation between 

pedestrian accidents and relevant Project 

Sidewalk data.(4)

© 2022 Project Sidewalk. Figure 3. Image. The Project 

Sidewalk crowdsourcing workflow showing potential map 

imagery input datasets, the crowdsourcing workflow, 

including labeling and validation missions, and how these 

human labels are used to train machine-learning algorithms.(4)

© 2022 Project Sidewalk. Figure 4. Image. Project Sidewalk 

user interface for labeling sidewalk accessibility using 

streetscape imagery.(4)

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  3
PRESENTATION 3—CITY OF AUSTIN 

SIDEWALKS AND GEOGRAPHIC 

INFORMATION SYSTEM (GIS) 

(JOHN EASTMAN, CITY OF AUSTIN, TX)

Mr. Eastman described various strongly supported 

policies in the city of Austin, TX, that allow the 

city to invest in moving toward a complete and 

well-maintained pedestrian network. In particular, an 

established ADA transition planning process that 

included sidewalk condition ratings has improved 

coordination across the city government and 

communication with the public. Images from the 

presentation can be seen in figure 5 and figure 6.

(1)

• Accessibility data has several 

interconnected uses, including route 

planning and wayfinding, community 

planning and investment activities, 

and advocacy efforts.

Presentation 2 Q&A Session 

Question 1: For Project Sidewalk, how are 

crowdsourcing volunteers recruited?(1)

Volunteers are generally recruited 

through local networks, often from 

disability service organizations.

Question 2: How can sidewalk data collection 

be scaled so that data do not end at the 

city limits?

Technologies are available. However, it 

is a matter of data standardization as 

well as political, legal, and funding 

considerations. In response to potential 

ADA challenges, some cities have 

proactively deployed data collection 

technology.(1) Existing ADA lawsuits have 

also generated new sidewalk data 

collection efforts. Community engagement 

efforts can also provide motivation. 

However, data collection capacity on 

a regional scale is an issue.

The presentation had the following 

major points: (1)

• Transit riders heavily rely on a strong 

pedestrian network. Many of Austin’s 

transit riders do not have the option 

to arrive at transit options using a 

vehicle, and many riders have mobility 

impairments.

• ADA transition planning for sidewalks in 

2009 and 2016 has supported Austin’s 

sidewalk improvement efforts, including 

the use of GIS. Sidewalk centerlines and 

curb ramp points are the primary data 

layers used. The city developed these 

layers based on the 2009 plan.(5) At 

the time, the city used GIS to determine 

where to allocate limited resources.

© 2022 City of Austin. Figure 5. Image. City of Austin, TX, 

sidewalk condition data collection.(1)

4  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
• Equity-focused prioritization metrics 

and condition data are used to inform 

sidewalk improvements. The city uses 

an equity-based approach in sidewalk 

investment decisions.

• Public-facing data are made available 

through a website to keep neighborhoods 

informed of upcoming and completed 

sidewalk improvements.

• Coordination with other capital 

improvement programs has amplified the 

impact of the sidewalk improvements.

• Sidewalk condition ratings (“excellent,” 

“good,” “marginal,” etc.) were developed 

by a consultant, as there was no standard 

option available. The city required these 

ratings as part of the 2016 plan. This 

plan included using GIS to determine the 

location of functionally deficient sidewalks 

as sites of potential improvement. These 

data have been used directly by 

maintenance crews.(5)

• The city led public awareness campaigns 

to inform property owners of their 

sidewalk maintenance responsibilities 

(e.g., clearing vegetation).(5)

Presentation 3 Q&A Session

Question 1: How does the city of Austin, TX, 

update sidewalk absence?

The city actively links development 

service performance data. High 

school interns update maps combined 

with satellite imagery and driveway 

permit data points. The city of Austin, TX, 

has a full-time GIS specialist in the 

sidewalk team to interface with the 

mapping layers.(1)

© 2022 City of

Austin. Figure 6. 

Image. City of 

Austin, TX, 

citywide sidewalk 

prioritization 

and condition 

assessment 

maps (2022).(1)

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  5
GENERAL WORKSHOP DISCUSSION Q&A

Question 1: What do researchers need from a 

partner to pilot a technology?

Researchers need multiple jurisdictions 

to get different perspectives on design, 

sizes, and context. The municipality, 

affected communities, and other parties

must collaborate, and all parties need to 

know how the data will be used. Some 

communities want to know where there 

is a need for sidewalk improvements, 

and some do not. Researchers must 

be willing to integrate other types 

of data and be open to data sharing. 

Some communities are not comfortable 

with data sharing.

Question 2: From a public agency perspective, 

what is the motivation for working with a research 

team on testing new technology?

Public agencies are motivated to use new 

technologies when they provide safe 

pedestrian access at an efficient cost or 

when the data allow us to communicate 

more effectively with our stakeholders.(1)

Question 3: How can smaller cities and rural 

areas effectively deploy these technologies?

• Increasing the capacity of smaller 

cities and rural areas to deploy these 

technologies through agencies pooling

resources or allowing State or county 

agencies to deploy technologies for 

cities or regions as a whole.

• Creating a more simplified and 

standardized approach for ADA transition 

plans.(1) Some who work in small towns 

have never heard of an ADA transition 

plan. For pavements, there is a simple 

methodology and approach. A template 

that tells people how to collect the data 

that they need, explains how to measure 

it, and describes how to lay it out is 

critical. Scale for the methodology also 

needs to be considered. Communities 

of 50,000 people have different needs 

than communities of 500,000.

• Using tools and data standards for 

monitoring. Often, staff will create a 

document or spreadsheet with these 

improvements, making it difficult to 

tell where these improvements are.

Question 4: What is the private sector’s role in 

deploying these technologies?

• Legal risk may be pushing much of the 

sidewalk mapping work.

• Technologies are available, but the extent 

to which Government can or should leverage 

those technologies or point entities in 

their direction has not been defined.

• The private sector plays a prominent 

role, but there should be data standards 

that companies must follow.

6  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
This white paper was developed in June 2021.

INTRODUCTION

FHWA’s EAR Program funds longer term, higher 

risk research with a high payoff potential. In 2012, 

the EAR Program funded three related projects 

on assistive wayfinding technologies for visually 

impaired users.(6) In support of the EAR Program, 

the Volpe Center (Volpe) coordinated a 

technology demonstration across the three 

projects in 2016 and subsequently completed a 

state of-the-art practice scan in 2017 to assess 

the opportunities for additional research. Both of 

these activities contributed to technology 

development in wayfinding and navigation as 

well as in pretrip and concierge services by 

FHWA’s Accessible Transportation Technologies 

Research Initiative program.(7)

Wayfinding and navigation technology 

research and development have been 

proceeding rapidly since the 2016 

demonstrations and the 2017 scan.(7) Both 

commercial providers and public agencies 

are collecting more spatial data than ever at 

increasingly higher resolutions. Advances in 

communication systems, machine learning, 

and sensor technology have also been 

contributing factors. Volpe developed the 

“Mapping and Navigation for Pedestrians with 

Disabilities” white paper to assist the EAR 

Program in assessing the state of emerging 

technologies for pedestrian mapping and 

wayfinding. As part of developing this white 

paper, Volpe will also coordinate with key 

stakeholders to identify government roles in 

research and development as well as potential 

next steps for the EAR Program.

NAVIGATION FOR PEDESTRIANS 

WITH DISABILITIES OVERVIEW

Modern smartphones equipped with 

Global Position System (GPS) receivers, 

navigation applications, detailed maps, 

and point-of-interest databases have made 

it easy to plan and navigate pedestrian routes 

that are accessible for many but not all users. 

These tools help outline potential routes from 

a user’s current place to a desired location. 

Typically, navigation applications can adapt 

their functionality according to various 

attributes, such as traffic congestion and 

method of travel. Still, until recently, they 

have emphasized vehicle-based rather than 

pedestrian navigation.

Some populations require more specialized 

data for safe navigation. People with sensory, 

mobility, and/or cognitive conditions may 

face additional challenges in safe wayfinding 

as pedestrians in indoor and outdoor 

environments. New technologies provide an

opportunity to address existing data gaps 

for safe pedestrian navigation for all users.

Current applications of indoor/outdoor 

pedestrian wayfinding and navigation assistance 

usually calculate the route to a destination 

based on the shortest or fastest path from the 

origin. Typically, outdoor wayfinding and 

navigation via mobile phone use established 

mapping interfaces created by private firms. 

However, these mapping applications may not 

always provide up-to-date maps, and they may 

not provide relevant features to all pedestrian 

users. Mobile applications dedicated to 

wayfinding in indoor environments have been 

preliminarily developed by private companies 

acquired by EsriTM. Such applications have been 

piloted at San Francisco International Airport 

in 2014 and Penn Station in New York City in 

2018.(8) These systems use low-energy wireless 

technology “beacons” and wireless network hot 

spots to help guide smartphone users through 

routing by voice in a mobile application.

Appendix. “Mapping and Navigation for Pedestrians with Disabilities” White Paper

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  7
Numerous findings in cognitive science show 

that the ease of use and communication of 

route instructions depends on factors other 

than the length of the route, such as the 

number and complexity of decision points.

INTENDED AUDIENCES

The intended audience for this research 

topic is public agencies that own and 

operate transportation facilities, such as 

roads, trails, transit stations, and airports. 

Other audiences include transportation 

professionals, information technology 

professionals, and researchers interested in 

finding ways to advance the collection of 

data valuable to pedestrian navigation and 

wayfinding. There are potential opportunities 

for data collection applications in almost 

every aspect of pedestrian navigation. The 

use of cooperative automation to assist in 

mapping out pedestrian features (e.g., stairs, 

steep inclines, ramps, etc.) focuses on new 

approaches that reduce the manual burden of 

data collection and increase automation or 

application of data. These new approaches 

may help alleviate obstacles to the 

widespread deployment of more accurate 

and useful pedestrian-focused map attributes 

and layers. These methods are either being 

privately tested or are being piloted by 

public agencies and the government to 

cost-effectively collect pedestrian navigation 

and wayfinding data in support of future 

infrastructure and policy efforts.

UNDERSTANDING USER NEEDS

Drivers are generally able to rely on 

dedicated vehicle navigation systems and 

mobile phone applications to reach their 

destination, whereas pedestrians are often 

a secondary audience for mapping and 

navigation providers. Although popular 

navigation tools provide pedestrian routes 

to destinations, suggested routes are not 

optimized for pedestrians with disabilities. 

People with disabilities often encounter 

numerous challenges when traveling 

independently, and understanding these 

challenges is essential to recognize when 

collecting wayfinding data.1

 For instance, 

obstacles, such as stairs, steep inclines, 

missing ramps, blocked curb ramps, or 

poor walking surfaces make sidewalks 

uncomfortable or even inaccessible to people 

with mobility disabilities.(9) The specific 

wayfinding needs of users with disabilities 

can vary depending on a person’s type of 

disability as well as personal preferences.

Due to the likelihood that a single obstacle 

could make a complete trip impossible for 

a pedestrian with disabilities, there are 

significant obstacles for people to find 

accessible routes.2 For example, in a recent 

study, interviewees with a visual disability 

stated that they might call ahead for 

directions to a destination. However, these 

directions were often given by a sighted 

respondent and frequently inadequate for a 

visually impaired traveler. Interviewees noted 

that, as a result, they typically would seek 

information from other visually impaired 

friends or trainers who understand helpful 

landmarks and routes to follow.3

Despite the availability of GPS-based 

information systems, pedestrians with 

disabilities still have unmet needs in terms 

of navigation. While regular and well-known 

routes generally work well, wayfinding 

breakdowns are possible. Rerouting and 

reorientation around obstacles are difficult 

activities without visual cues. Unfamiliar 

routes are difficult because of the lack of 

mental maps or accommodations.3

8  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
LANDSCAPE OF CURRENT 

DATA COLLECTION PRACTICES 

AND APPROACHES

Collecting accurate sidewalk and associated 

contextual pedestrian data is an ever-evolving 

practice that is a key element in providing 

pedestrians with disabilities the ability to 

travel independently. The following section 

highlights the type of data being collected 

and their collection process. It covers several 

data collection practices and approaches, 

including examples provided to Volpe 

from the University of Washington and 

Atlanta Regional Commission during a 

wayfinding-focused ITS4US Complete Trip 

Deployment Program meeting in April 2021.4

Data Collection Methods

Municipalities may encompass hundreds 

to thousands of miles of sidewalk networks 

which vary in condition. To best serve 

pedestrians with various disabilities, multiple 

variables about the physical environment 

must be captured to allow for effective trip 

planning and safe navigation. The data 

collection process depends on the resources 

available to the data collectors. Satellite 

imagery is often used to identify roadways 

with paralleling sidewalk systems to capture 

a base map. Sidewalk networks can also 

be constructed using parcel-level land use 

and roadway centerline data. These data 

are typically incomplete; onsite mapping 

fills the gaps in the satellite imagery, parcel, 

and roadway data.

Once a map is established, manually pushing a 

wheeled device is the most common method 

of collecting the characteristic sidewalk 

data. These devices can be equipped with 

various data collection tools, such as cameras, 

gyroscopes, GPS, or LiDAR. Georgia Tech has 

used such a device to map Atlanta’s sidewalk 

system, where data and video are collected 

by attaching an application-equipped tablet 

to a standard wheelchair and walking it along 

any desired route.(10)

According to the Georgia Tech Center for 

Transportation Operations and Safety, once the 

sidewalks have been video recorded by the 

wheeled device, general-public volunteers, 

undergraduates, and graduate students review 

the video and code the sidewalk manually. 

These manual adjustments took roughly 

1,200 h to complete for the city of Atlanta, GA.5

Researchers at the University of Washington 

have taken a similar approach, but employed 

machine-learning software to identify particular 

sidewalk characteristics. This system uses a 

convolutional neural network (CNN) computer 

vision tool that has helped to semiautomate 

and make less labor intensive these data 

collections.3 However, some data, such as curb 

ramp data, are generally entered manually.

Sidewalk delivery robots have been identified 

as a potential way to further automate the 

sidewalk data collection process. Additionally,

LiDAR drones could streamline the data 

collection process. However, due to the high

cost of LiDAR drones, this method is too 

expensive for many municipalities.6

Type of Data Collected

The needs of a particular pedestrian with a 

disability vary depending on their preferences 

and abilities. The city of Seattle, WA, and 

University of Washington have partnered to 

create the open-source tool OpenSidewalks, 

housed on OpenStreetMap.(11) From an initial 

scan, these partners have developed a 

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  9
significant, open-source pedestrian navigation 

tool. Table 1 highlights the data this project 

has collected in Seattle.

The Taskar Center for Accessible Technology 

(TCAT) at the University of Washington has 

developed a GitHub data schema where 

anyone can insert data into OpenStreetMap 

to create pedestrian maps and analyses.(12, 13)

The University of Washington team stated 

that there are six active OpenSidewalks 

collections underway in Redmond, WA; 

Bellevue, WA; Austin, TX; Korfu, Greece; 

Padova, Italy; and Milan, Italy.7 Although 

much of the data collected is related to the 

navigation of sidewalks, OpenSidewalks also 

provides the opportunity to code the location 

of indoor elevators and the hours of operation 

of such elevators. Moreover, recent literature 

provides relevant analysis on the current 

extent of municipality-level collection and 

maintenance of accessibility data.(14)

REVIEW OF SPECIFIC DATA COLLECTION 

ENTITIES AND/OR EFFORTS

OpenSidewalks (System in Development)

The aforementioned data architecture, 

OpenSidewalks, is a notable example of 

remote collection. The effort, currently 

being developed by TCAT, seeks to make 

pedestrian infrastructure, like sidewalks, of 

“equal consideration in an open, routable 

Phase I

Fundamental Pedestrian Layer

Phase II

Primary Pedestrian Features

Phase III

Application Specific

Footways:

• Sidewalks

• Crossings 

(best crossing locations)

• Footway links (paths that 

go through curb ramps)

• Curb lowered/raised

• Outdoor stairs/escalators

• Outdoor ramps/ 

wheelchair ramps

• Publicly accessible 

buildings with elevators 

(interior or exterior)

• Connection of all components 

to represent a connected 

transportation graph layer

• Footpath surface information

• Elevation information

• Cross slope (cross grade)

• Surface type

• Large surface interruptions

• Sidewalk width

 Transit stops:

• Node location

• Footway link to the rest of 

pedestrian transportation network

• Complexities specifying the 

actual path to getting to the 

transportation stop, including 

going underground, etc.

• Amenities and descriptors 

of the stop

• Entrances to public buildings 

and amenities

Shoring:

• Tree lined

• Grass lined

• High curbed

• Curb present

• Other separators

Pedestrian corners and 

pedestrian signalization:

• Crossing type

• Indicating pedestrian 

crossing time

• Signals indicating to traffic 

that pedestrians are crossing

• Are there accessible 

pedestrian signals?

• Sidewalk bulbs

• Pedestrian islands

• Lit corners

Table 1. The table shows three phases of data creation for different layers of sidewalk data.(1)

10  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
transportation network from which we can ask 

a variety of important questions. This means 

collecting a connected network of path types 

with detailed attributes like width, surface 

composition, steepness, and shared traffic.”(11)

AccessMap (Available in Seattle, WA; Bellingham, WA; 

and Mt. Vernon, WA)

AccessMap serves as an example of an 

open-source data collection method. This mobile 

application prioritizes pedestrian-mobility data 

and “provides customized accessible sidewalk 

and footpath routing directions” based on a 

“personal mobility profile.”(15) The application 

layers multiple forms of pedestrian needs and 

preferences for travel and allows for 

automated route functionality. AccessMap 

answers the question: How do I get from point 

A to point B in a nonmotorized mode given my 

specific needs and preferences? For example, 

AccessMap identifies the best path for users 

by allowing them to alter their route based 

on their preferences for steepness. Figure 7

provides a representation of AccessMap and 

how pedestrians can use it for trip planning.

Esri (System is a Commercial Product)

Esri’s ArcGIS is a GIS and geodatabase 

management company that uses mobile 

applications and remote sensing technology 

to collect and store mapping data via the 

cloud.(14) ArcGIS allows developers to upload a 

basemap and join it with other basemaps or 

additional GIS layers as they are packaged 

by other sources. In addition to basemaps, 

ArcGIS can use geodatabases, which present 

building assets in relation to real-world 

environments. This function can enhance the

understanding of pedestrian needs within 

the built environment. The datasets stored 

by Esri can be cross-referenced with existing 

geotagged data to suit the mapping needs of 

the user. Esri’s remote sensing data collection 

capabilities include LiDAR technology, which 

can be later paired with existing datasets.(16)

For instance, users can download elevation 

data from the U.S. Geological Survey (USGS) 

and create three-dimensional imagery of 

an area that LiDAR has scanned.8 These data 

allow individuals to generate a point cloud 

to color code and classify different features. 

This technology also allows for analyzing 

elevation data within a specific area. These 

data would enable users to identify certain 

routes with high levels of incline and decline.

SideGuide (System in Development)

SideGuide is a large-scale sidewalk dataset 

that offers the opportunity to help people 

with disabilities. SideGuide was generated 

through data acquired from smartphone 

cameras and a ZED stereo camera. To 

validate SideGuide, a prototype trained 

© 2021 Taskar Center for Accessible Technology. 

Figure 7. Screenshot. AccessMap—the interface 

to Seattle’s sidewalks.

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  11
with SideGuide data was developed to 

aid people with disabilities by informing 

users of their proximity to an obstacle. 

The prototype recognizes target objects on 

the sidewalk, and it predicts the distance 

to each object and the average distance 

within the object region.(17)

RECOMMENDATIONS

This paper reviewed current practices 

and innovative technology development 

of data collection for pedestrian navigation 

and wayfinding tools that help people 

with disabilities. It is a starting point for 

discussing the application and feasibility 

of using collective information for 

transportation safety data. Rather than 

capture the entire universe of active 

research and development in this field, the 

authors of this white paper only focused 

on some uses of these applications for 

transportation agencies in collecting 

pedestrian navigation and wayfinding 

data. For example, this paper did not 

discuss how the rise in automated vehicles 

will likely coincide with the rise in people 

with disabilities traveling independently. 

Additionally, there are aspects of the 

collected information that are not 

addressed in this paper, such as how the 

public can be persuaded to participate 

in the collection and transmission of 

data for these technologies.

Academic efforts, like the University of 

Washington’s and Georgia Tech’s ITS4US 

awards, have demonstrated the ability to 

partner with transit agencies, universities, 

and the private sector to make strides 

forward in accessible pedestrian navigation.(6) 

In the future, these dynamic relationships 

can create innovative and novel approaches, 

making wayfinding and pedestrian navigation 

data collection efforts easier and more 

efficient. Moving forward, it will be important 

to build on these dynamic relationships while 

also continuing to balance innovation with 

the public good.

In an increasingly urbanized United States, 

identifying replicable data collection 

methods will remain important for providing 

an equitable pedestrian network. As a result, 

scaling up pedestrian navigation and 

wayfinding technologies should be 

considered. In May 2021, FHWA developed a 

pedestrian and object detection request that 

identified research groups in the academic, 

public, and private sectors theorizing, 

developing, or working with approaches for 

gathering data (e.g., video, image, computer

vision, camera) to use in intelligent 

transportation systems.(18)

Based on the initial exploration detailed 

in this paper, collective pedestrian and 

wayfinding applications may provide a 

tremendous opportunity for agencies to 

collect data effectively and implement 

infrastructure improvements that enhance 

the safety of people with disabilities.

12  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
1. Wayfinding is a process of following a 

path or route between an origin and 

destination that becomes extremely 

difficult for people with disabilities.(19)

2. The complete trip concept reflects the 

understanding that a person’s travel 

comprises a chain of steps beginning 

with an often-spontaneous decision to 

make a trip through to planning an 

itinerary; traversing the built environment 

and its transportation networks (with or 

without a vehicle); and navigating streets, 

intersections, facilities, stations, and stops 

to their destination safely, efficiently, 

and carefree.(20) The complete trip is the 

realization that if any part of the tripmaking chain is broken, the trip cannot 

be completed, and an opportunity is lost.

3. This information was shared with the 

ITS4US Complete Trip team during a 

pedestrian navigation information 

session in April 2021.

4. The ITS4US Complete Trip Deployment 

Program is a program that aims to solve 

mobility challenges for all travelers with 

a specific focus on underserved 

communities, including people with 

disabilities, older adults, low-income 

individuals, rural residents, veterans, and 

limited English proficiency travelers.(21) 

This program will enable communities 

to build local partnerships, develop and 

deploy integrated, replicable mobility 

solutions, and help to achieve complete

trips for all travelers.

5. This information was shared with the 

ITS4US Complete Trip team during a 

meeting in February 2021.

6. This insight was gained from a discussion 

with the Georgia Tech and University of 

Washington ITS4US teams in April 2021.

7. The locations of active OpenSidewalks 

collections were shared with the ITS4US 

team in April 2021. Additionally, 

OpenSidewalks data collection is slated 

to begin soon in Los Angeles, CA; 

Quito, Ecuador; Sao Paulo, Brazil; and 

Santiago, Chile.(22)

8. USGS data do not include sidewalk-specific 

data but rather a three-dimensional image of 

an area that may have identifiable sidewalks.

NOTES:

EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT  |  13
REFERENCES

1. FHWA. 2022. “Mapping and Navigation for Pedestrians 

with Disabilities “ (workshop). Virtual, October 19 

and 20, 2022.

2. FHWA. 2022. “Americans with Disabilities Act (ADA) 

and Section 504 of the Rehabilitation Act of 1973 

(504)” (web page). https://www.fhwa.dot.gov/civilrights/

programs/ada/, last accessed April 6, 2023.

3. Sanborn. n.d. “Planimetric Feature Extraction” (web 

page). https://www.sanborn.com/planimetric-featureextraction/, last accessed March 6, 2023.

4. Project Sidewalk. n.d. “Project Sidewalk” (website). 

https://sidewalk-sea.cs.washington.edu/, last accessed 

March 6, 2023.

5. City of Austin. 2016. Sidewalk Master Plan/ADA

Transition Plan Update. Austin, TX: City of Austin. 

https://www.austintexas.gov/sites/default/files/files/

Public_Works/Street_%26_Bridge/Sidewalk_MPU_

Adopted_06.16.2016_reduced.pdf, last accessed 

April 10, 2023.

6. FHWA. 2015. Assistive Technologies for Visually 

Impaired Persons. Report No. FHWA-HRT-15-040. 

Washington, DC: FHWA.

7. U.S. Department of Transportation. 2017. “U.S. 

Department of Transportation Announces Accessible 

Transportation Technologies Research Initiative 

Application Development Awards” (web page). 

https://www.transportation.gov/briefing-room/dot7117, 

last accessed March 6, 2023.

8. Esri. n.d. “ArcGIS Indoors” (web page). https://www.

esri.com/en-us/arcgis/products/arcgis-indoors/

overview, last accessed April 10, 2023.

9. Tajgardoon, M., and H. A. Karimi. 2015. “Simulating 

and Visualizing Sidewalk Accessibility for Wayfinding 

of People with Disabilities.” International Journal of 

Cartography 1, no. 1: 79–93. https://doi.org/10.1080/23

729333.2015.1055646, last accessed March 6, 2023.

10. Georgia Institute of Technology. n.d. “Sidewalks” (web 

page). https://transportation.ce.gatech.edu/node/32, 

last accessed March 6, 2023.

11. Taskar Center for Accessible Technology. n.d. 

OpenSidewalks Editing. Seattle, WA: University 

of Washington. https://www.opensidewalks.com/

files/01_OpenSidewalksEditing.pdf, last accessed 

March 6, 2023.

12. Github. n.d. “OpenSidewalks-Schema” (web page). 

https://github.com/OpenSidewalks/OpenSidewalksSchema, last accessed March 6, 2023.

13. Taskar Center for Accessible Technology. 2021. “Start 

Mapping” (web page). https://www.opensidewalks.com/

mapping/, last accessed March 6, 2023.

14. Deitz, S., A. Lobben, and A. Alferez. 2021. “Squeaky 

Wheels: Missing Data, Disability, and Power in the 

Smart City.” Big Data & Society 8, no. 2. https://doi.

org/10.1177/20539517211047735, last accessed 

March 6, 2023.

15. Lin, J. 2021 “AccessMap” (web page). https://tcat.

cs.washington.edu/2021/12/03/accessmap/, last 

accessed March 6, 2023.

16. Esri. n.d. “A Quick Tour of LiDAR in ArcGIS” (web 

page). https://desktop.arcgis.com/en/arcmap/10.3/

manage-data/las-dataset/a-quick-tour-of-lidar-in-arcgis.

htm, last accessed March 6, 2023.

17. Park, K., Y. Oh, S. Ham, K. Joo, H. Kim, H. Kum, and 

I. S. Kweon. 2020. “SideGuide: A Large-Scale Sidewalk 

Dataset for Guiding Impaired People.” Presented at the 

IEEE/RSJ International Conference on Intelligent 

Robots and Systems (IROS). Las Vegas, NV: Institute 

of Electrical and Electronics Engineers. https://ras.

papercept.net/images/temp/IROS/files/1873.pdf, last 

accessed March 6, 2023.

18. FHWA. 2023. “Image Data of Pedestrian Behavior” 

(web page). https://sam.gov/opp/8ddea74f47b

84c3b8596d1fa7680bd74/view, last accessed 

September 15, 2023.

19. Golledge, R. G., ed. 1999. Wayfinding Behavior: 

Cognitive Mapping and Other Spatial Processes. 

Baltimore, MD: Johns Hopkins University Press.

20. Federal Transit Administration. n.d. “Integrated Mobility 

Innovation” (web page). https://www.transit.dot.gov/

IMI#:~:text=The%20Complete%20Trip%20concept%20

reflects,a%20vehicle)%3B%20navigating%20

streets%2C, last accessed September 18, 2023.

21. U.S. Department of Transportation. n.d. “ITS4US” 

(website). https://its.dot.gov/its4us/htm/overview.htm, 

last accessed September 18, 2023.

22. Taskar Center for Accessible Technology. 2023. 

“OpenSidewalks” (web page). https://tcat.

cs.washington.edu/opensidewalks-2/, last accessed 

September 18, 2023.

14  | EAR PROGRAM—SIDEWALK MAPPING FOR PEDESTRIAN NAVIGATION—WORKSHOP SUMMARY REPORT
Getting Involved with the EAR Program

To take advantage of a broad variety of scientific and engineering discoveries, the EAR 

Program involves both traditional stakeholders (State department of transportation 

researchers, University Transportation Center researchers, and Transportation Research 

Board committee and panel members) and nontraditional stakeholders (investigators from 

private industry, related disciplines in academia, and research programs in other countries) 

throughout the research process.

Learn More

For more information, see the EAR Program website at https://highways.dot.gov/research/

exploratory-advanced-research. The site features information on research solicitations, 

updates on ongoing research, links to published materials, summaries of past EAR 

Program events, and details on upcoming events.
EAR Program Results

As a proponent of applying ideas across traditional research fields to 

stimulate new problem-solving approaches, the EAR Program strives to 

develop partnerships with the public and private sector. The program 

bridges basic research (e.g., academic work funded by National Science 

Foundation grants) and applied research (e.g., studies funded by State 

DOTs). In addition to sponsoring projects that advance the development 

of highway infrastructure and operations, the EAR Program is committed 

to promoting cross-fertilization with other technical fields, furthering 

promising lines of research, and deepening vital research capacity.

EXPLORATORY ADVANCED RESEARCH

FHWA-HRT-23-084

HRTM-30/09-23(WEB)E

Recommended citation: Federal Highway Administration, 

Sidewalk Mapping for Pedestrian Navigation (Washington, DC: 2023) 

https://doi.org/10.21949/1521389