Google Maps Landmark Redesign

UX Research, UI/UX Design, Prototyping

March - April 2025

Figma & Miro

Google Maps Landmark Redesign

UX Research, UI/UX Design, Prototyping

March - April 2025

Figma & Miro

Research

Listening to Users & Key Insights

  • I surveyed 43 users (ages 18-29), to find out "Where do users struggle most in transit navigation?" using Google Maps.

  • Results:

  • 48% are confused by walking directions

  • 44% are unclear about streetcar transfers

  • 58% reported inaccurate updates

Some responses from my first survey regarding Google Maps transit navigation

Defining the Opportunity

  • Since 46.5% of users felt confused by the walking directions between different modes of transit, what if there was a way to simplify these directions using real-world landmarks?

  • Doing so may allow users to identify where the streetcar or bus stop is more easily.


Validating the Idea - Do Landmarks Work?

I conducted a second survey with 32 users using a mix of closed- and open-ended questions.

  • Overall results:

  • 96.9% said landmarks make directions easier

  • 72% feel more confident using them

  • Top landmarks users prefer seeing in navigation directions are coffee shops, restaurants, and retail shops


Some responses expressing interest in landmark based navigations

Process

Feasibility Check — Can Google Support It?

Google Places API VS Directions API

  • To evaluate whether landmarks could be used as a navigation feature, I first researched how Google Maps works and where it sources its information.

  • I compared the Google Places API (for landmarks) and Directions API (for routes).

Google Places → identifies landmarks → Google Directions → generates route → Combined → contextual instructions

  • The main difference between the two is that Places API is for info and data about places (such as landmarks), while Directions API is for directions and routes (using these landmarks for example)

  • Together, they can dynamically surface landmarks along walking paths, so this feature is feasible.

Mapping the Experience with User Flows

  • The only change to the existing flow: a new “Walking Details” button that reveals landmark-enhanced directions (pictured below in purple).

  • I integrated the feature seamlessly into the Google Maps ecosystem — users don’t need to learn a new tool

User Flow with the "Walking Details" Feature

Structuring the Experience Before Visual Design

  • Before moving into high-fidelity design, I explored the structure of the experience at a low fidelity to validate where landmark-based information should live within Google Maps — without introducing new cognitive load.

    At this stage, my focus was not on visuals, but on:

    • How users transition between walking and transit modes

    • When landmark details are most helpful

    • How to surface additional context without disrupting Google Maps’ existing flow

    The goal was to ensure the feature felt additive, not overwhelming.

Early Concept: Detailed Walking Directions (Low Fidelity)

  • Rather than relying solely on the blue dotted walking line, I tested an early concept that paired the live map with step-by-step written walking instructions, including recognizable landmarks.

    This allowed users to:

    • Confirm they walking in the correct direction

    • Visually orient themselves using familiar places

    • More easily locate streetcar stops after exiting the subway.

    At a low fidelity stage, this concept helped validate the core idea before investing in a more polished UI.

After clicking "details" (pictured on left), users will see a detailed walking instructions page (pictured on right)

  • This checkpoint confirmed that landmark-based instructions added clarity without requiring users to learn a new interaction pattern.

Designing Within Real-World Constraints

While exploring potential features, I also considered technical and data limitations that affect transit navigation.

From earlier survey responses, users frequently mentioned that real-time transit updates felt inaccurate, particularly when using TTC data. This feedback highlighted a trust gap in live tracking features, where reliability depends heavily on data shared by transit agencies through GTFS Realtime feeds (General Transit Feed Specification - transit agencies use this to share data with apps like Google Maps). In Toronto, limited TTC data availability further impacts how precise these updates can be.

Through competitor analysis, I observed that Moovit mitigates this issue by incorporating crowdsourced data, allowing users to report delays and conditions in real time. While this approach can improve accuracy, it relies on active user participation and falls outside the scope of Google Maps’ existing data ecosystem.

Rather than designing an unrealistic solution, I treated live transit tracking as a supporting feature, reinforcing the primary goal: helping users confidently reach the correct stop through clearer, landmark-based walking directions.

This constraint-driven approach ensured the concept remained feasible while working within Google Maps’ existing ecosystem and real-world data limitations.

Transitioning to High-Fidelity Design

Once the structure and placement of landmark-based walking details were validated, I translated the concept into high-fidelity designs that aligned closely with Google Maps’ visual language.

The interface prioritizes:

  • Familiar layout patterns

  • Clear hierarchy between map, text, and actions

  • Optional access to detailed walking directions through an existing interaction (“Walking Details”)

This ensures the feature feels native to Google Maps rather than an experimental add-on.


The structure of landmark-based walking instructions remained consistent from low-fi to hi-fi — visual polish was added without changing the underlying flow.

  • This also ensures users could access additional context exactly when they needed it — without interrupting navigation.

High-Fidelity Prototype & Walkthrough

The final prototype demonstrates a complete journey to Bloom Café, including:

  • Searching for a destination

  • Selecting transit options (506 streetcar)

  • Starting navigation

  • Accessing Detailed Walking Directions through the map icon

  • Following landmark-enhanced instructions

  • Viewing live progress once onboard the streetcar

  • Arriving at the destination with journey feedback

Landmarks are integrated directly into written instructions while maintaining the live map view, bridging the gap between digital navigation and real-world orientation.

The Final Prototype

Outcome & Reflection

This project reinforced the importance of designing navigation systems that support how people naturally think and move through space.

Key takeaways:

  • Contextual cues like landmarks can significantly reduce uncertainty during transit transfers

  • Small, well-placed features can meaningfully improve confidence without increasing complexity

  • Designing within platform constraints leads to more realistic and defensible solutions

As part of this work, I also explored motion as a communication tool. This was my first time creating an animation in Figma, used to simulate live location progress once the user boards the streetcar — mirroring how Google Maps visually communicates movement during a journey.

By grounding design decisions in user research, feasibility checks, low-fidelity validation, and exploratory motion design, this concept demonstrates how thoughtful UX improvements can enhance everyday transit experiences.

Thank you for reading!


Research

Listening to Users & Key Insights

  • I surveyed 43 users (ages 18-29), to find out "Where do users struggle most in transit navigation?" using Google Maps.

  • Results:

  • 48% are confused by walking directions

  • 44% are unclear about streetcar transfers

  • 58% reported inaccurate updates

Some responses from my first survey regarding Google Maps transit navigation

Defining the Opportunity

  • Since 46.5% of users felt confused by the walking directions between different modes of transit, what if there was a way to simplify these directions using real-world landmarks?

  • Doing so may allow users to identify where the streetcar or bus stop is more easily.


Validating the Idea - Do Landmarks Work?

I conducted a second survey with 32 users using a mix of closed- and open-ended questions.

  • Overall results:

  • 96.9% said landmarks make directions easier

  • 72% feel more confident using them

  • Top landmarks users prefer seeing in navigation directions are coffee shops, restaurants, and retail shops


Some responses expressing interest in landmark based navigations

Process

Feasibility Check — Can Google Support It?

Google Places API VS Directions API

  • To evaluate whether landmarks could be used as a navigation feature, I first researched how Google Maps works and where it sources its information.

  • I compared the Google Places API (for landmarks) and Directions API (for routes).

Google Places → identifies landmarks → Google Directions → generates route → Combined → contextual instructions

  • The main difference between the two is that Places API is for info and data about places (such as landmarks), while Directions API is for directions and routes (using these landmarks for example)

  • Together, they can dynamically surface landmarks along walking paths, so this feature is feasible.

Mapping the Experience with User Flows

  • The only change to the existing flow: a new “Walking Details” button that reveals landmark-enhanced directions (pictured below in purple).

  • I integrated the feature seamlessly into the Google Maps ecosystem — users don’t need to learn a new tool


User Flow with the "Walking Details" Feature

Structuring the Experience Before Visual Design

  • Before moving into high-fidelity design, I explored the structure of the experience at a low fidelity to validate where landmark-based information should live within Google Maps — without introducing new cognitive load.

    At this stage, my focus was not on visuals, but on:

    • How users transition between walking and transit modes

    • When landmark details are most helpful

    • How to surface additional context without disrupting Google Maps’ existing flow

    The goal was to ensure the feature felt additive, not overwhelming.

Early Concept: Detailed Walking Directions (Low Fidelity)

  • Rather than relying solely on the blue dotted walking line, I tested an early concept that paired the live map with step-by-step written walking instructions, including recognizable landmarks.

    This allowed users to:

    • Confirm they walking in the correct direction

    • Visually orient themselves using familiar places

    • More easily locate streetcar stops after exiting the subway.

    At a low fidelity stage, this concept helped validate the core idea before investing in a more polished UI.

After clicking "details" (pictured on left), users will see a detailed walking instructions page (pictured on right)

  • This checkpoint confirmed that landmark-based instructions added clarity without requiring users to learn a new interaction pattern.

Designing Within Real-World Constraints

While exploring potential features, I also considered technical and data limitations that affect transit navigation.

From earlier survey responses, users frequently mentioned that real-time transit updates felt inaccurate, particularly when using TTC data. This feedback highlighted a trust gap in live tracking features, where reliability depends heavily on data shared by transit agencies through GTFS Realtime feeds (General Transit Feed Specification - transit agencies use this to share data with apps like Google Maps). In Toronto, limited TTC data availability further impacts how precise these updates can be.

Through competitor analysis, I observed that Moovit mitigates this issue by incorporating crowdsourced data, allowing users to report delays and conditions in real time. While this approach can improve accuracy, it relies on active user participation and falls outside the scope of Google Maps’ existing data ecosystem.

Rather than designing an unrealistic solution, I treated live transit tracking as a supporting feature, reinforcing the primary goal: helping users confidently reach the correct stop through clearer, landmark-based walking directions.

This constraint-driven approach ensured the concept remained feasible while working within Google Maps’ existing ecosystem and real-world data limitations.

Transitioning to High-Fidelity Design

Once the structure and placement of landmark-based walking details were validated, I translated the concept into high-fidelity designs that aligned closely with Google Maps’ visual language.

The interface prioritizes:

  • Familiar layout patterns

  • Clear hierarchy between map, text, and actions

  • Optional access to detailed walking directions through an existing interaction (“Walking Details”)

This ensures the feature feels native to Google Maps rather than an experimental add-on.


The structure of landmark-based walking instructions remained consistent from low-fi to hi-fi — visual polish was added without changing the underlying flow.

  • This also ensures users could access additional context exactly when they needed it — without interrupting navigation.

High-Fidelity Prototype & Walkthrough

The final prototype demonstrates a complete journey to Bloom Café, including:

  • Searching for a destination

  • Selecting transit options (506 streetcar)

  • Starting navigation

  • Accessing Detailed Walking Directions through the map icon

  • Following landmark-enhanced instructions

  • Viewing live progress once onboard the streetcar

  • Arriving at the destination with journey feedback

Landmarks are integrated directly into written instructions while maintaining the live map view, bridging the gap between digital navigation and real-world orientation.

The Final Prototype

Outcome & Reflection

This project reinforced the importance of designing navigation systems that support how people naturally think and move through space.

Key takeaways:

  • Contextual cues like landmarks can significantly reduce uncertainty during transit transfers

  • Small, well-placed features can meaningfully improve confidence without increasing complexity

  • Designing within platform constraints leads to more realistic and defensible solutions

As part of this work, I also explored motion as a communication tool. This was my first time creating an animation in Figma, used to simulate live location progress once the user boards the streetcar — mirroring how Google Maps visually communicates movement during a journey.

By grounding design decisions in user research, feasibility checks, low-fidelity validation, and exploratory motion design, this concept demonstrates how thoughtful UX improvements can enhance everyday transit experiences.

Thank you for reading!


Research

Listening to Users & Key Insights

  • I surveyed 43 users (ages 18-29), to find out "Where do users struggle most in transit navigation?" using Google Maps.

  • Results:

  • 48% are confused by walking directions

  • 44% are unclear about streetcar transfers

  • 58% reported inaccurate updates

Some responses from my first survey regarding Google Maps transit navigation

Defining the Opportunity

  • Since 46.5% of users felt confused by the walking directions between different modes of transit, what if there was a way to simplify these directions using real-world landmarks?

  • Doing so may allow users to identify where the streetcar or bus stop is more easily.


Validating the Idea - Do Landmarks Work?

I conducted a second survey with 32 users using a mix of closed- and open-ended questions.

  • Overall results:

  • 96.9% said landmarks make directions easier

  • 72% feel more confident using them

  • Top landmarks users prefer seeing in navigation directions are coffee shops, restaurants, and retail shops


Some responses expressing interest in landmark based navigations

Process

Feasibility Check — Can Google Support It?

Google Places API VS Directions API

  • To evaluate whether landmarks could be used as a navigation feature, I first researched how Google Maps works and where it sources its information.

  • I compared the Google Places API (for landmarks) and Directions API (for routes).

Google Places → identifies landmarks → Google Directions → generates route → Combined → contextual instructions

  • The main difference between the two is that Places API is for info and data about places (such as landmarks), while Directions API is for directions and routes (using these landmarks for example)

  • Together, they can dynamically surface landmarks along walking paths, so this feature is feasible.

Mapping the Experience with User Flows

  • The only change to the existing flow: a new “Walking Details” button that reveals landmark-enhanced directions (pictured below in purple).

  • I integrated the feature seamlessly into the Google Maps ecosystem — users don’t need to learn a new tool

User Flow with the "Walking Details" Feature

Structuring the Experience Before Visual Design

  • Before moving into high-fidelity design, I explored the structure of the experience at a low fidelity to validate where landmark-based information should live within Google Maps — without introducing new cognitive load.

    At this stage, my focus was not on visuals, but on:

    • How users transition between walking and transit modes

    • When landmark details are most helpful

    • How to surface additional context without disrupting Google Maps’ existing flow

    The goal was to ensure the feature felt additive, not overwhelming.

Early Concept: Detailed Walking Directions (Low Fidelity)

  • Rather than relying solely on the blue dotted walking line, I tested an early concept that paired the live map with step-by-step written walking instructions, including recognizable landmarks.

    This allowed users to:

    • Confirm they walking in the correct direction

    • Visually orient themselves using familiar places

    • More easily locate streetcar stops after exiting the subway.

    At a low fidelity stage, this concept helped validate the core idea before investing in a more polished UI.

After clicking "details" (pictured on left), users will see a detailed walking instructions page (pictured on right)

  • This checkpoint confirmed that landmark-based instructions added clarity without requiring users to learn a new interaction pattern.

Designing Within Real-World Constraints

While exploring potential features, I also considered technical and data limitations that affect transit navigation.

From earlier survey responses, users frequently mentioned that real-time transit updates felt inaccurate, particularly when using TTC data. This feedback highlighted a trust gap in live tracking features, where reliability depends heavily on data shared by transit agencies through GTFS Realtime feeds (General Transit Feed Specification - transit agencies use this to share data with apps like Google Maps). In Toronto, limited TTC data availability further impacts how precise these updates can be.

Through competitor analysis, I observed that Moovit mitigates this issue by incorporating crowdsourced data, allowing users to report delays and conditions in real time. While this approach can improve accuracy, it relies on active user participation and falls outside the scope of Google Maps’ existing data ecosystem.

Rather than designing an unrealistic solution, I treated live transit tracking as a supporting feature, reinforcing the primary goal: helping users confidently reach the correct stop through clearer, landmark-based walking directions.

This constraint-driven approach ensured the concept remained feasible while working within Google Maps’ existing ecosystem and real-world data limitations.

Transitioning to High-Fidelity Design

Once the structure and placement of landmark-based walking details were validated, I translated the concept into high-fidelity designs that aligned closely with Google Maps’ visual language.

The interface prioritizes:

  • Familiar layout patterns

  • Clear hierarchy between map, text, and actions

  • Optional access to detailed walking directions through an existing interaction (“Walking Details”)

This ensures the feature feels native to Google Maps rather than an experimental add-on.


The structure of landmark-based walking instructions remained consistent from low-fi to hi-fi — visual polish was added without changing the underlying flow.

  • This also ensures users could access additional context exactly when they needed it — without interrupting navigation.

High-Fidelity Prototype & Walkthrough

The final prototype demonstrates a complete journey to Bloom Café, including:

  • Searching for a destination

  • Selecting transit options (506 streetcar)

  • Starting navigation

  • Accessing Detailed Walking Directions through the map icon

  • Following landmark-enhanced instructions

  • Viewing live progress once onboard the streetcar

  • Arriving at the destination with journey feedback

Landmarks are integrated directly into written instructions while maintaining the live map view, bridging the gap between digital navigation and real-world orientation.

The Final Prototype

Outcome & Reflection

This project reinforced the importance of designing navigation systems that support how people naturally think and move through space.

Key takeaways:

  • Contextual cues like landmarks can significantly reduce uncertainty during transit transfers

  • Small, well-placed features can meaningfully improve confidence without increasing complexity

  • Designing within platform constraints leads to more realistic and defensible solutions

As part of this work, I also explored motion as a communication tool. This was my first time creating an animation in Figma, used to simulate live location progress once the user boards the streetcar — mirroring how Google Maps visually communicates movement during a journey.

By grounding design decisions in user research, feasibility checks, low-fidelity validation, and exploratory motion design, this concept demonstrates how thoughtful UX improvements can enhance everyday transit experiences.

Thank you for reading!


Project info

The Challenge

Pain point: Users rely on Google Maps’ blue walking line but struggle when street signs or TTC transfers are unclear.

Goal: Simplify multi-modal navigation by adding recognizable *landmarks* to walking instructions.

Understanding the Landscape

Google Maps has the widest coverage. Transit app focuses on commuters using public transit. Moovit crowdsources real-time data - but none use landmarks.


The Challenge

Pain point: Users rely on Google Maps’ blue walking line but struggle when street signs or TTC transfers are unclear.

Goal: Simplify multi-modal navigation by adding recognizable *landmarks* to walking instructions.


Understanding the Landscape

Google Maps has the widest coverage. Transit app focuses on commuters using public transit. Moovit crowdsources real-time data - but none use landmarks.