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Pharos: Improving Navigation Instructions on Smartwatches by Including Global Landmarks


Abstract and Figures

Landmark-based navigation systems have proven benefits relative to traditional turn-by-turn systems that use street names and distances. However, one obstacle to the implementation of landmark-based navigation systems is the complex challenge of selecting salient local landmarks at each decision point for each user. In this paper, we present Pharos, a novel system that extends turn-by-turn navigation instructions using a single global landmark (e.g. the Eiffel Tower, the Burj Khalifa, municipal TV towers) rather than multiple, hard-to-select local landmarks. We first show that our approach is feasible in a large number of cities around the world through the use of computer vision to select global landmarks. We then present the results of a study demonstrating that by including global landmarks in navigation instructions, users navigate more confidently and build a more accurate mental map of the navigated area than using turn-by-turn instructions.
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Pharos: Improving Navigation Instructions on
Smartwatches by Including Global Landmarks
Nina Wenig, Dirk Wenig,
Steffen Ernst, Rainer Malaka
Digital Media Lab, TZI
University of Bremen
{nwenig, dwenig, malaka}
Brent Hecht
People, Space, and Algorithms
(PSA) Computing Research
Northwestern University
Johannes Sch¨
Human-Computer Interaction
University of Bremen
Landmark-based navigation systems have proven benefits re-
lative to traditional turn-by-turn systems that use street names
and distances. However, one obstacle to the implementation of
landmark-based navigation systems is the complex challenge
of selecting salient local landmarks at each decision point for
each user. In this paper, we present Pharos, a novel system
that extends turn-by-turn navigation instructions using a single
global landmark (e.g. the Eiel Tower, the Burj Khalifa, mu-
nicipal TV towers) rather than multiple, hard-to-select local
landmarks. We first show that our approach is feasible in a
large number of cities around the world through the use of
computer vision to select global landmarks. We then present
the results of a study demonstrating that by including global
landmarks in navigation instructions, users navigate more con-
fidently and build a more accurate mental map of the navigated
area than using turn-by-turn instructions.
ACM Classification Keywords
H.5.2. Information Interfaces and Presentation (e.g. HCI):
User Interfaces — input devices and strategies, interaction
Author Keywords
Global Landmarks; Landmark-based Navigation; Computer
Vision; Smartwatches; Pedestrian Navigation
Research across many fields has robustly established that
landmarks are an essential means by which humans naviga-
te through their environments [11, 15, 16, 46]. People of all
ages use landmarks in this fashion, and landmarks have been
described as “the key to the ability to orient oneself and to na-
vigate in an environment.” [48]. This literature has motivated
researchers to develop a series of landmark-based navigation
technologies that have been shown to outperform traditional
MobileHCI ’17, September 04-07, 2017, Vienna, Austria
©2017 Copyright is held by the owner/author(s).
ACM ISBN 978-1-4503-5075-4/17/09.
Figure 1: The Pharos navigation approach: Using Google
Street View imagery, the visibility of global landmarks (here
a municipal TV tower) is determined and then included in
the turn-by-turn pedestrian navigation instructions for smart-
turn-by-turn instructions in a number of studies, particularly
in the case of pedestrian navigation [17, 43, 52, 53].
However, despite the success of these research prototypes,
well-known navigation technologies largely do not incorporate
landmarks. This is in part due to the substantial implementa-
tion challenges associated with automating landmark-based
navigation. For instance, landmarks that are salient for each
individual user must be selected [55, 58], the utility of spe-
cific landmarks for navigation is often gender- and language-
dependent [55], and assessing the visibility of each landmark
is dicult [38, 59].
In this paper, we introduce the Pharos pedestrian navigation
approach, which seeks to maintain the benefits of landmark-
based navigation while substantially increasing the tractability
of landmark-based navigation systems. The key to the Pharos
approach is to reduce the challenges associated with landmark-
based navigation by utilizing global landmarks [24, 30], a
class of landmarks that have not yet been considered in the
navigation technology literature. The landmarks that are tradi-
tionally utilized are local landmarks [58] and are always loca-
ted along route segments, particularly at key decision points.
Global landmarks, on the other hand, are landmarks that can
be seen from significant distances and can be located far from
the user’s route. Typical global landmarks are tall buildings
(e.g. the Eiel Tower, the Burj Khalifa, municipal TV towers),
but mountains (e.g. Corcovado mountain in Rio de Janeiro,
or the when there are mountain ranges in the background as
in Denver, Seattle, Bishkek, Cape Town) or whole downtown
areas (e.g. the skyline of New York) can also serve as global
In addition to addressing the limitations of local landmarks
discussed above (e.g. language/gender dependency), global
landmarks also have several other important properties that
are beneficial with respect to navigation tasks. First, global
landmarks are constant, meaning that users can update their
position and orientation relative to a single landmark rather
than jumping from landmark to landmark. Secondly, because
the distance to the landmark is not an important component
of global landmark-based navigation, global landmarks can
be used as a sort of compass for orientation [49]. Finally,
while landmark-enriched navigation instructions require that
the device’s positioning system is able to locate the user with
high accuracy for local landmarks, this is not true for global
landmarks. A positioning error of a few meters might heavily
influence the visibility of a local landmark (e.g. a shop or
a street sign), but the direction of a distant global landmark
remains the same.
This paper demonstrates that the Pharos approach is both
(1) feasible and (2) has benefits compared to traditional turn-
by-turn (i.e. no landmark) instructions. We establish feasibility
by using computer vision (CV) and machine learning (ML)
techniques to show that global landmark visibility is su-
ciently extensive within cities and, critically, automatically
detectable at scale with good accuracy. We also show that
our approach works with publicly-available images and al-
lows for pre-computation server-side, meaning that no further
computation on a mobile or wearable device is needed.
We develop a prototype of the Pharos approach and use it to
establish the eectiveness of global landmark-based instruc-
tions through a field-based user study. Focusing on the use
case of pedestrian navigation using smartwatches, we find
that global landmark-enriched instructions outperform state-
of-the-art turn-by-turn instructions along several key metrics.
Specifically, we find that while both approaches had roughly
similar performance in terms of navigation speed and number
of errors, as hypothesized, Pharos outperformed turn-by-turn
with respect to navigation confidence and the accuracy of the
user’s resultant mental map of the area.
To summarize, the contribution of this paper is four-fold:
We introduce the Pharos navigation approach, which makes
landmark-based navigation much more feasible by using
global landmarks (relative to traditional landmark-based
approaches that use local landmarks).
We show that global landmarks are suciently broadly
visible for pedestrian navigation tasks and that the locations
at which global landmarks are visible can be computed
at scale from geotagged imagery using computer vision
and machine learning. This information can be computed
beforehand so that no processing on the wearable or mobile
device is needed.
We develop a pipeline to generate navigation instructions
for pedestrians on smartwatches.
We demonstrate through a user study that the Pharos glo-
bal landmark-based navigation approach outperforms the
current-state-of-the-art turn-by-turn instructions in import-
ant navigation metrics.
Below, we first introduce related work. We then describe the
process by which we determine the visibility of global land-
marks and create visibility maps. We next discuss the Pharos
approach of integrating global landmarks into navigation in-
structions and demonstrate the benefits of our approach based
on the results of a user study. Finally, we conclude with a
discussion of the Pharos approach, landmark-based pedestrian
navigation in general, and open problems in this area.
This work draws from and builds on research in the domains
of (1) pedestrian navigation and mobile guides, (2) landmark-
based navigation in particular, and (3) landmark detection.
Below, we discuss each of these areas in turn.
Mobile Guides and Pedestrian Navigation
Research on pedestrian navigation dates back almost two deca-
des. From the beginning of this work, landmarks have played
a role in guiding the user. For example, the GUIDE project [7]
aimed at integrating landmarks in textual navigation instruc-
tions. Malaka and Zipf [34] used a 3D city model to create
navigation instructions not only incorporating the visibility but
also the look of landmarks (e.g. “turn right after the red buil-
ding”) for pre-selected situations. Similarly, the LOL@ tourist
guide for mobile devices [40] enhanced routing information
with references to landmarks. The PhotoMap [45] uses images
of taken with a GPS-enhanced mobile phone as background
maps for on-the-fly navigation tasks.
While one of the earliest mobile guides, the DeepMap sys-
tem [34], used a wrist-mounted display, wearable devices have
become more relevant in research in the last five years. Indeed,
in the context of pedestrian navigation, wearable devices do
have an important benefit over their mobile counterparts as the
user’s hands can remain mostly free. Wenig et al. [56] introdu-
ced StripeMaps, a cartographic approach for indoor navigati-
on with smartwatches, which transforms 2D maps with route
information into a 1D stripe. McGookin and Brewster [35]
investigated unidirectional navigation for runners by designing
a navigation system called RunNav, which could also be used
on a smartwatch. RunNav does not provide explicit routes, but
rather a high-level overview to inform runners of areas that
are good and bad places to run.
Even though research has explored how other modalities can
be used to provide navigation instructions (e.g. auditory in-
structions [22] or haptic clues for mobile [42] and wearable
devices [29, 39]), map-based navigation for mobile devices
and turn-by-turn instructions for wearable devices have been
established as de facto standards. For example, the current
version of Google Maps for Android Wear smartwatches uses
turns, street names, and distances to provide the user with
instructions such as: “After 20 m turn left into Denver Road”.
Importantly, current navigation systems and applications typi-
cally do not include landmarks in their instructions.
Landmark-based Navigation
There is already a large corpus of related work exploring the
benefits of landmark-based navigation for pedestrians. While
the term landmark was originally used to dierentiate features
with outstanding characteristics [33], the meaning of the term
has changed over time and is now used more generically to
describe well-known places [15]. According to Sorrows and
Hirtle [48], “it is useful to understand landmarks in a way that
supersedes knowledge in the environment”.
Landmark-based navigation instructions have been investi-
gated in depth from the perspective of spatial cognition and
cognitive psychology. Tom and Denis [52] showed that for gui-
ding pedestrians, route information referring to streets is less
eective (regarding the number of stops, instruction checks
and time) than route information referring to local landmarks.
In another experiment by Tom and Denis [53], the participants
processed landmark-based instructions faster than street-based
instructions and also remembered the route better with land-
mark information than with street information. Additionally,
Ross et al. [43] showed that adding landmarks to basic pe-
destrian navigation instructions (turn information and street
names) results in less errors and a higher user confidence. In
contrast to our work, neither Tom and Denis [52, 53] nor Ross
et al. [43] relied only on global landmarks in the navigation
From a more applied perspective, Wither at al. [59] showed
that people can navigate solely using landmarks highlighted
in panoramic imagery. Even ‘in the wild’ in natural environ-
ments, as shown by Snowdon and Kray [47], there are types of
landmarks which are feasible to be used in mobile navigation
systems. Recently, Bauer et al. [2] have found evidence, that
indoors pedestrian navigation instructions on mobile devices
should only depict a single prominent landmark (instead of
four) for high navigation eciency. In contrast, the Pharos
approach is primarily targeted at navigation in urban environ-
ments. While our work is related to the work of Wither at
al. [59], it is landmark-centric and instead of local landmarks
the Pharos approach relies on global ones.
Landmarks do also play a role in designing pedestrian naviga-
tion aids for people with disabilities. For example, landmarks
can be used to support low-vision and blind people in loca-
ting bus stops [18] or to help mobility impaired users with
navigating [19]. In addition, landmarks can be used for other
purposes in location-based services. For example, Kray and
Kortuem [25] interactively determined the user’s positions
based on the visibility of nearby landmarks. Lu et al. [32]
generated traveling routes from geo-tagged photographs.
In general, landmarks have to be chosen carefully as their se-
lection is highly important for the resulting quality of navigati-
on instructions [36] (e.g. the personal salience of a landmark
could be eected by language or gender [55]). More specifi-
cally, Sorrows and Hirtle [48] examined landmarks in real and
electronic spaces and classified landmarks in terms of visual,
cognitive and structural dimensions. Global landmarks have
to be outstanding in all dimensions. Steck and Mallot [49]
investigated the role of global and local landmarks in virtual
environment navigation. They found out that in virtual environ-
ments both local and global landmarks are used for wayfinding,
but dierent participants rely on dierent strategies; some of
the participants used only local landmarks while others only
used global ones. This results in a key question for our work,
as with Pharos we aim at improving pedestrian navigation
instructions with only a single global landmark rather than
multiple local landmarks.
Landmark Detection
Detecting landmarks is a crucial step in the pipeline of all
location-based services relying on landmarks. While resear-
chers have often opted to perform the selection of landmarks
by hand [34, 36] or using crowd-based [18] approaches, com-
mercially successful applications require a solution that works
on a global scale across a large set of users. As such, some
automated approaches have been developed that make use of
large databases of geographic features (e.g. OpenStreetMap
[OSM]) [10, 41]. For example, Raubal and Winter [41] explo-
red landmark-enriched wayfinding instructions by automati-
cally extracting local landmarks from a spatial dataset based
on a formal measure for landmark saliency. More recently,
ager and Koller [10] generated landmark-based navigation
instructions for car navigation systems using OSM data. They
used proximity to certain geographic features as a criterion to
select landmarks. For prominent landmarks, the proximity can
be used to estimate if a geotagged image shows a particular
landmark [38].
As global landmarks can be seen from longer distances and
therefore the mere location information is not sucient, visi-
bility tests are always necessary. Visual landmark detection is
related to the problems of location recognition, object reco-
gnition and image retrieval. For computer vision it is common
to use simple image features, such as edges or corners in an
image. For example, SIFT [31], SURF [3] and ORB [44] are
popular image feature descriptors, which can be used together
with machine learning approaches, e.g. support vector ma-
chines [5] to predict whether an image contains a particular
landmark. Most recently convolutional neural networks [26]
have become very popular; they work directly on the pixel
Approaches for detecting dierent landmarks are often ba-
sed on these computer vision algorithms in combination with
the location of the landmark [6] or other contextual informati-
on [28]. Zheng et al. [60] built a large database from geotagged
photos to detect the most popular landmarks around the world.
As landmarks are visible from dierent positions, the visibi-
lity from dierent angles has to be computed [23]. Wither
et al. [59] automatically created landmark-based navigation
instructions by detecting salient landmarks in panoramic street
imagery by using additional data, such as LiDAR information
and text understanding. Such 3D information can often be used
to enhance computer vision algorithms, e.g. [21]. Wakamiya
et al. [55] combined this information also with social data
(Twitter or Foursquare) to determine the best local landmarks
in an area.
In this section, we describe the Pharos global landmark-
based navigation approach. Pharos is named after the famous
lighthouse in Alexandria that was one of the “seven wonders”
of the ancient world and was used for centuries as a navigation
landmark for ships. Below, we first report on a comparison
of methods to determine the visibility of global landmarks.
Secondly, we report on how we include global landmarks in
the navigation instructions.
Determining Visibility for Global Landmarks
We determined the visibility of selected global landmarks to
investigate whether they are (a) sucient visible for pedestrian
navigation tasks and (b) that this visibility can be detected at
scale from geotagged imagery. The first step for this, is to
compute their visibility for a given region of interest (ROI),
e.g. the inner city or a district.
Global landmarks are diverse and range from mountains and
large buildings to entire downtowns. The visibility of global
landmarks is dependent on their prominence in a ROI (rather
than their absolute height). Prominence in a topographcial
context describes “the height of a mountain...summit by the
vertical distance between it and the lowest contour line en-
circling it but containing no higher summit within it”[1].
Although global landmarks are often visible from large parts
of a given ROI, they are not unconditionally visible from areas
around the landmark as other buildings/structures might block
line of sight. To create detailed visibility maps for global land-
marks, we evaluated dierent computer vision and machine
learning techniques to determine their visibility for a certain
We compared the most common image features used in object
detection tasks in combination with machine learning on three
dierent landmarks: The Eiel Tower in Paris (324 m, France),
the Petronas Towers in Kuala Lumpur (452 m, Malaysia) and
the Burj Khalifa in Dubai (828 m, United Arab Emirates). All
three are very prominent landmarks, but have very dierent
characteristics (e.g. height, appearance, symmetry and struc-
ture). For each landmark, we manually collected as training
images 150 (not necessarily geotagged) images containing the
landmark using Google image search and Flickr. We made
sure that the images for each landmark were taken in dierent
Sliding Window Selective Search
SURF+SVM 75.39 73.47
SIFT+SVM 69.26 60.70
ORB+SVM 54.47 52.73
CNN 84.13 77.18
Table 1: Comparison of methods to determine the visibility
of global landmarks. The
1-score is the harmonic mean of
precision and recall. Our refined convolutional neural network
outperforms the other approaches. In general, the sliding win-
dow approach works better than selective search.
lighting and weather conditions as well as from multiple per-
spectives and distances. In addition, as negative samples, we
collected 450 images from Google Street View (GSV), which
do not contain the landmarks but instead typical content in the
ROI around the landmarks.
With this training data we compared SIFT [31], SURF [3] and
ORB [44] as image features in combination with a Support
Vector Machine [5] and an approach using a convolutional
neural network [26] (i.e. deep learning). For deep learning, it
is common to use already-trained neural networks and refine
them by training again on task-specific data to improve ac-
curacy. Therefore, we used the convolutional neural network
which was trained on the ImageNet data [8] and trained it in
a second step with our dataset. For this, we used the DeCaf
library [9].
We used a simple sliding window approach, in which we slide
a window with the size 256
in 64
steps over the
image to create sub-images. Then, we tested for each sub-
image whether it contains the landmark or not. We did this for
dierent resolutions of the image by using an image pyramid
to detect the landmark, even if the landmark is far away and
very small or when it is larger than 256
in the GSV
image. We tested the sliding window against the selective
search algorithm by Uijlings et al. [54] to find the landmarks
in sub-images.
Classifier Evaluation
We used a separate data set with 100 manually collected GSV
images for each landmark to evaluate the classifiers. We cate-
gorized an image as containing a particular landmark when
at least one sub-image (based on the sliding window or se-
lective search approach) contains the landmark. The results
of the comparison can be seen in Table 1; the
1-score is the
harmonic mean of precision and recall. We calculated the sco-
res across all three landmarks. The evaluation shows that we
achieve the best results with the convolutional neural network
combined with a simple sliding window approach. All results
are similar for each landmark (
1-score Eiel Tower: 77
Petronas Towers: 79
5, Burj Khalifa: 94
5). The results for the
Burj Khalifa are particularly good due to its prominence in
Dubai’s skyline. Overall, our results suggest that the visibility
of global landmarks can robustly be detected at scale from
geotagged images.
(a) (b) (c)
Figure 2: Visibility maps, automatically produced based on a CNN classifying Google Street View (GSV) images, for the three
selected global landmarks: a) Eiel Tower b) Burj Khalifa and c) Petronas Towers. Green spots indicate GSV images that contain
the global landmark. Aerial images from Google Maps were used as base map (©Google 2016).
Visibility of Global Landmarks
As the results of the technical evaluation showed that the
convolutional neural network works best, we used it to create
visibility maps around the three landmarks using GSV images.
In a radius of 2 km around the landmark, we downloaded a
GSV image at regular 100m intervals. This resulted in around
1600 images for every ROI. For some points or areas GSV
is not available. After excluding these areas, we computed
the visibility of the landmark in all the remaining images.
Figure 2 shows the resulting visibility maps for all three global
landmarks. The green dots indicate that the landmark is visible
in the GSV image. For the three selected global landmarks, the
visibility maps show that these landmarks are likely suciently
visible for pedestrian navigation tasks.
As the three selected global landmarks dier in shape, size and
contour line of the surroundings, we conclude that determining
the visibility of global landmarks based on public available
imagery is feasible from the technical point of view. The vi-
sibility maps can be created on map and navigation servers
beforehand so that no processing on the wearable or mobile
device is needed. The visibility information than can be used
to include global landmarks in navigation instructions.
Including Global Landmarks in Smartwatch
Navigation Instructions
Turn-by-turn navigation instructions inform the user about
upcoming turns, usually combined with a notification (e.g.
vibration). Global landmarks can be included in these instruc-
tions to confirm that the user has taken the correct turn and is
still on the correct route. For Pharos, we developed a pipeline
to include the visibility information of the global landmarks
in turn-by-turn based pedestrian navigation instructions. Whi-
le our approach could be applied to both car and pedestrian
navigation systems, we opted to focus on the integration of
global landmarks into instructions for pedestrian navigation.
More specifically, we focused on smartwatch-based pedestrian
navigation as smartwatches have important benefits relative
to other mobile devices (e.g. smartphones) for navigation, e.g.
they remove the need to constantly take a device out of one’s
purse or pocket [56] (Note: we expect that our findings will
generalize to a smartphone context, although further research
is necessary to comform this hypothesis).
Current navigation systems for smartwatches, e.g. Google
Maps for Android, use turn-by-turn-based instructions. They
primarily rely on simple arrows showing the direction the user
has to take at the next turning point (not at other decision points
where the user does not have to turn), combined with the name
of the street on which the user has to turn. Integrating global
landmarks into such navigation instructions is not a trivial task.
In general, situations in which navigation instructions can only
rely on the global landmark are rare (e.g. “Head towards the
landmark” when the route leads the user directly towards the
landmark). Therefore, for Pharos, we aimed at enriching turn-
by-turn navigation instruction with direct and indirect hints
related to global landmarks. Direct hints precisely include the
landmark in the navigation instruction, while indirect hints can
be seen as additional information on the landmark’s position
relative to the user at decision points.
Textual instructions, especially on very small screens, need to
be both short and understandable. For Pharos, we identified
four dierent types of textual navigation instructions including
a global landmark: the route heads towards towards or away
from the global landmark, the global landmark is on the left
or on the right, the global landmark is in a direction other than
a cardinal direction (e.g. soft left), and the global landmark is
not visible at the turning point.
The most simple situations are situations when the user, after
a turn, walks straight towards the landmarks or walks away
from the landmark. For such instructions, it is straightforward
to add a direction to turn-by-turn instructions:
“Head towards the landmark” or
“Head away from the landmark”
Similar are situations in which the landmark is on the left or
on the right of the user after the turn, but cannot be directly
included in the instruction. For such situations, we add indirect
hints about the landmark’s location, e.g.:
“The landmark will be on your right” or
“The landmark will be on your left”
More dicult are situations in which the landmark is neither in
front or behind the user nor on the left or on the right (e.g. 45
degrees or 135 degrees to the user’s walking direction). Here,
the English language struggles to unambiguously describe
such situations without referring to angles. To address these
situations, we use instructions such as:
“The landmark will be in front of you to your right” or
“The landmark will be behind you on your left”
Additionally, there will be situations in which the landmark is
not visible at the turning point. For these situations, we propose
to include landmark information in the following form:
“At the end of the street,
the landmark will be in front of you”
From the technical point of view, all instructions can be easily
generated using the visibility maps. For each turning point
the angle between the direction of the following path segment
(towards the next turning point) and the direction towards the
landmark indicates the kind of instruction that should be used.
For the last type of instruction, the visibility during the path
segment also has to be considered. Whenever the landmark is
not visible, simple turn-by-turn instruction can be used.
We performed a user study to evaluate the benefits of the
Pharos approach and to explore how the inclusion of global
landmarks changes the navigation experience. The study was
focused on standard navigation evaluation metrics (time to
reach destination, number of errors made) and the users’ con-
fidence that they were on the correct route. Confidence is not
only an important aspect for the usability of a system in gene-
ral [4], it is particularly important for navigation systems (and
its instructions) as the user is usually navigating in an unfami-
liar environment with potential safety risks (e.g., other trac
participants). In addition, we also measured how well users
built up spatial knowledge of the route using cognitive maps.
Spatial knowledge supports users in performing the same or
a similar navigation task without technological assistance the
next time they are in the area and also aids them in identifying
possible shortcuts [15].
We compared the following two conditions in the user study,
illustrated in Figure 3.
Turn-by-turn navigation instructions (TBT) as a baseline.
Turn-by-turn navigation instructions enriched with global
landmarks through the Pharos approach (PHA).
In both conditions, the instructions are based heavily on Goo-
gle Maps navigation instructions for Google Android Wear
smartwatches. We used the same instructions as generated by
Google Maps for Android Wear as well as the exact “look
and feel” in the baseline (TBT) but enriched these instructions
with the global landmarks in the PHA condition.
In the current version of Google Maps for Android Wear,
the upcoming turn is shown with the remaining distance in
(a) (b)
Figure 3: The two dierent conditions compared in the user
study. Turn-by-turn navigation (TBT) on the left and the turn-
by-turn navigation instructions including global landmarks
following the Pharos approach on the right (PHA).
meters (in steps of 10 m). For the user study, we decided
against this approach for two reasons. First, in pre-tests, the
distance measures and the position of the notifications were
insucienctly accurate as they occurred often with an oset
of around 10 m. Second, pedestrian navigation instructions
should neither require nor encourage the users to constantly
check the system, distracting them from their primary task of
To achieve optimal comparability, in the baseline condition
(TBT) as well in the PHA condition we followed a wizard-of-
oz study approach with instructions manually triggered by an
experimenter. Participants were notified of new instructions
via vibration of the smartwatch.
Participants & Apparatus
The study was conducted in a residential district of Bremen
(Germany), a mid-sized city in northern Europe. As a global
landmark, we used a telecommunication and television tower
(referred as TV tower in the rest of the paper), which is about
235 meters high. It can be seen from significant distances (see
Introduction & Motivation). The ROI of the study features
wide and narrow streets with mid-sized row houses (up to
three or four stories) such that the global landmark is not
visible at all times.
For the user study, we selected two routes. Each of the routes
had a very similar lengths (route 1: 1.12 km, route 2: 1.17 km)
and featured an equal number of turns (seven turns plus the
start and end point). The maximal length between two turns
on both routes was 0.3 km. The global landmark was similar-
ly visible in both routes. Figure 4 shows both routes on the
visibility map for the TV tower.
The turn-by-turn-based navigation instructions were created
as follows. First, we used the multi-destination feature of
Google Maps to create the route. Secondly, we followed the
route using Google Maps for Android Wear and at all turning
(a) (b)
Figure 4: The two routes used in the user study. Green dots indicate the visibility of the TV tower based on GSV images. Route 1
(a, red) has a length of 1.12 km and route 2 (b, blue) is 1.17 km long. For both routes, Ais the starting point and Iis the end point.
Google Maps was used as base map (©Google 2016).
points we took a screenshot of the smartwatch (including
the background image showing the turn in addition to the
arrow, see Figure 3). We used these screenshots to create the
instructions and (for both conditions) removed the countdown
distance measures (see above). For the Pharos condition, we
enriched the instructions with information about the location
of the TV tower (as described in the previous section). At
one of the turning points and the following route segment of
route 1, the landmark was not visible at all. At this point we
used the baseline TBT instruction also in the PHA condition.
The instructions for the next turning point were shown imme-
diately before the turn at the exact same locations (manually
triggered by the experimenter accompanied with a vibration
notification of the new instruction). As a result, on the next
route segment (after the turn) the instruction would be outda-
ted. To exclude irritations because of outdated instructions,
for the path segments between the turning points we slightly
adapted the navigation instructions for both conditions. These
navigation instructions were based on the instructions for the
previous turning point, but the turning arrow was replaced with
a straight ahead arrow. Furthermore, we changed the tense of
the textual instruction from future tense to present tense (e.g.
“The TV tower is on your right” instead of “The TV tower will
be on your right”). In contrast to the turning point instructi-
ons, these in-between instructions were not accompanied by
In accordance with our wizard-of-oz study design, we built
a simple Android Wear smartwatch app to present the navi-
gation instructions. An Android companion app we built for
smartphones (showing all the turn-by-turn-based navigation
instructions in a list view) allowed the experimenter to select
and send the instructions to the smartwatch. The experimen-
ter triggered the instructions always at the same predefined
spots, approximately 5 m before the turning point. Furthermo-
re, the app allowed the experimenter to count how often the
participants looked at the smartwatch.
We recruited 12 participants (4 females, 8 males) with an ave-
rage age of 27.1 (
S D=
5). Most of our participants had very
limited familiarity with the study area, although some had
traveled along minor segments of the routes before. All partici-
pants own a smartphone and two of the participants regularly
wear a smartwatch, but only one of them had navigated via
smartwatch before. Ten of the participants use a smartphone
for navigation purposes on a regular basis.
The user study was conducted with a LG G Watch. All parti-
cipants performed the test in both conditions (within-subject
design). The orders of the two conditions (TBT and PHA) as
well as the two routes (route 1 and route 2) were counterbalan-
Task & Procedure
The participants were introduced to the experiment and told
to follow the two dierent routes, one after the other. We
explained the navigation task but did not mention the role of
the TV tower. The participants did not have to select a route or
target destination on a mobile device or on the smartwatch. For
both conditions, we oriented the participants in the direction
of the first movement and then started the navigation task with
the first instruction.
As participants walked the route, the experimenter followed a
few meters behind, selecting the navigation instructions on the
companion app, collecting timing information, counting the
number of times the user looked at her watch, and assessing
the number of navigation errors. An error was assessed when
a participant took a wrong turn without noticing their mistake
after 10 meters (after this point, the experimenter would then
guide the participant back on the route). While they were
navigating, we asked the participants to rate their confidence
(i.e. whether or not they believed they were on the correct
route) on a seven-point scale. We performed this confidence
assessment three times: after the start instruction, in the middle
of the route, and at the end of the route.
!"#$% !$#$&
27.6 19.6
Error bars: +/-1SD
!"# !"$
89.6 90.8
!"#$ !%#"
NASA-TLX Frustration
Error bars: +/-1SD
Figure 5: The results for the baseline (TBT) and Pharos (PHA) conditions for (a) the time the participants needed to complete the
routes with both interfaces, (b) how often they looked at the smartwatch, and (c) how confident they felt during navigation. The
second row shows the results from (d) the System Usability Scale, (e) the overall NASA-TLX Score, and (f) the results for the
NASA-TLX sub-scale of frustration.
After each condition, participants were asked to draw a cogniti-
ve map of the route they were instructed to follow. In the PHA
condition, they were also asked to include the location of the
TV tower. The concept of cognitive maps was first introduced
by Tolman [51] and later adapted and extended to the domain
of spatial computing, where cognitive maps are a “mental re-
presentation of people’s perception of the real world” [14, 13].
They provide a representation of the spatial knowledge of a
user [37]. The goal was to measure if the users gained more
spatial knowledge in the PHA condition.
We also used the NASA-TLX [20] to measure the perceived
workload and the System Usability Scale (SUS) [4] to measure
the perceived usability. All questionnaires were filled out for
both conditions after the participants had drawn the cognitive
map. The total time taken by each participant for the whole
study was about 60 minutes. Participants were encouraged to
think aloud and to ask questions if necessary. Noteworthy inci-
dents were recorded in writing. A semi-structured interview
was conducted with each of the participants after finishing
both routes.
Results & Analysis
All participants were able to complete all the tasks. Figure 5
summarizes the results of the user study.
On average, the participants took 14 minutes and 4 seconds
per route (Route 1: 14
, Route 2: 13
). The fastest par-
ticipant needed 11 minutes and 15 seconds while the slowest
one needed 17 minutes and 50 seconds. Participants made a
maximum of two errors per route. Most of the errors happe-
ned on route 1 (
92) due to a missing sidewalk at one
turning point and only one participant made an error on route
2 (
08). The participants were slightly faster in the PHA
condition compared to TBT (TBT:
S D=
S D=
), while they committed al-
most the same number of errors in both conditions (TBT:
S D=
65; PHA:
S D=
5). Statistical analy-
sis did not reveal significant dierences in either the speed or
error rate measures.
Regarding the perceived usability, the SUS scores were high
for both the TBT condition (
S D=
2) and the PHA
condition (
S D=
8), see Figure 5d. That means that
the participants had no serious usability problems in both
conditions influencing the results. Regarding the perceived
taskload, the NASA-TLX values are low and almost the sa-
me for both the TBT condition (
S D=
7) and the
PHA condition (
S D=
0), the sub-scale of frustra-
tion (“How insecure, discouraged, irritated, stressed, and an-
noyed were you?”) diers (TBT:
S D=
6; PHA:
S D=
8), see Figures 5e and 5f. This means that
both conditions evoke a low workload with a slightly higher
frustration for the TBT condition. However, statistical analysis
did neither reveal a significant dierence for the sub-scale of
frustration, nor for the NASA-TLX overall values and the SUS
The confidence ratings resulted in three values per partici-
pant per route. On average, the confidence was higher in the
Pharos condition (
S D=
1) than in the TBT condi-
tion (
S D=
4), see Figure 5c. A paired t-Test revea-
led that the dierence is statistically significant (
001) with a large eect size
81 (Cohen’s
Figure 6: Mental map route sketches for route 2 from all participants in the baseline (TBT) and Pharos (PHA) conditions (rotated
and scaled for comparability). First row TBT and second row PHA condition.
243). The reduced confidence in the TBT instructions is
also apparent in the number of looks at the navigation instruc-
tions on the smartwatch. The number of looks is substantially
lower for the PHA condition (
S D=
6) than for the
TBT condition (
S D=
6), see Figure 5b. A paired
t-test revealed a statistically significant dierence between the
conditions (
039), with a medium eect size
36 (Cohen’s
74). In other words, with the Pharos
approach of integrating global landmarks into the navigation
instructions, participants were not only more confident that
they are on the correct route, they also looked at their smart-
watch less often than was the case with baseline turn-by-turn
instructions. We note that looking at one’s smartphone is often
substantially more eortful than doing so using a smartwatch,
so we expect that the benefits of Pharos may be greater in this
respect for smartphone-based navigation.
To assess and analyze the representation of the spatial know-
ledge of a user while performing the task we used cogniti-
ve maps [37]. The cognitive maps drawn by the participants
after each condition were digitized using QGIS
. We perfor-
med a bi-dimensional regression following the procedure as
described by Friedman [12]. In general, bi-dimensional re-
gression [50] “requires an equal number of points between
the configurations to be related”[14]. However, this was not
true across participants’ sketches of the whole route. The-
refore, we performed a bi-dimensional regression only on
the turning points and not on the segments between them.
A Wilcoxon signed-rank test on the retrieved correlation va-
lues of the TBT (
,S T D=
17) and PHA condition
,S T D=
11) showed a significant dierence between
these two conditions (
043). That means that
participants could better remember the route with the Pharos
approach than with turn-by-turn instructions. Exemplarily, Fi-
gure 6 shows the sketches drawn by the participants for route
2 in the TBT and PHA condition.
Qualitative Feedback
In addition to observations, after walking both routes and
testing both variants of instructions, we conducted a semi-
structured interview with the participants. Overall they were
very satisfied with smartwatch navigation and felt very sa-
fe: “I do not have to remember the way, better than with a
smartphone [...]” as navigation instructions on a smartphone
have to be constantly checked (P6). One participant stated that
“smartwatch navigation is really cool” (P7).
When asking the participants which of the two prototypes they
prefer, the answers were mixed, even though PHA outperfor-
med TBT in terms of confidence and spatial knowledge. Seven
of them preferred the PHA condition and five the TBT instruc-
tions. Participants who preferred the TBT condition thought
that the additional information is “unnecessary” (P9, P10),
because they have “more to read”. However, the participants
thought that Pharos is extremely helpful especially at the star-
ting point, whether they preferred PHA in general (P4, P11)
or TBT (P10).
In this paper, we present a novel way to include global land-
marks in pedestrian navigation instructions, which makes
landmark-based navigation much more feasible than approa-
ches using local landmarks. From a technical point of view,
we have shown that the visibility of global landmarks can be
determined automatically from existing and public available
geotagged image content. Our approach of using a convo-
lutional neural network combined with a sliding window is
robust and easy to train for new landmarks, as it requires only
around 100 images for each landmark. In addition, we presen-
ted a complete pipeline to not only determine the visibility of
global landmarks, but also to include them in the navigation
instructions. It allows to compute the visibility of landmarks
beforehand on map and navigation servers so that no further
computation on a mobile or wearable device is needed.
From the user’s perspective, Pharos oers a lightweight but
eective navigation support. In a user study, the Pharos ap-
proach outperformed current-state-of-the-art turn-by-turn in-
structions in important navigation metrics. We showed that
small textual changes to the navigation instructions including
hints on the location of global landmarks led to significantly
more confident users. Additionally, although the Pharos navi-
gation instructions contain more information than traditional
turn-by-turn instructions, the participants looked less often
at their smartwatch while navigating. We also saw that these
changes resulted in users building a better spatial knowledge
of their environment. Regarding time and error, we did not find
any dierences. This is not surprising, as a perfectly working
turn-by-turn-based navigation system — also in the baseline
condition the navigation instructions were manually triggered
with a very high accuracy current state-of-the-art pedestrian
system do not provide (see User Study) — is probably impossi-
ble to outperform in these metrics. However, we are convinced
that there is a need for navigation systems providing additio-
nal values, as Pharos does, but without resulting in slower
navigation and more errors.
Additionally, all participants of the user study lived in the city
the study took place, but were not familiar with most parts of
the testing sites. As such, our study suggests that Pharos can be
helpful when traveling to unknown places. This is particularly
true in places where traditional navigation techniques break
down (e.g. the street signs are written in a dierent language
or with dierent characters).
The user study has also highlighted the benefits of smart-
watches for pedestrian navigation in general as already pointed
out by related work [56, 57]. The participants liked navigating
via smartwatch and positively mentioned that, when using a
smartwatch, they have their hands free for other interactions.
Additionally, the study has also revealed current problems
of pedestrian navigation systems for smartwatches that could
be overcome by using Pharos. Due to relatively low position
accuracy, they constantly provide the user with information
about the next decision point, which leads to low confidence
and might prevent the user to build up spatial knowledge of
their environment.
We evaluated the Pharos approach with global landmarks using
large buildings. However, mountains or downtown skylines
are global landmarks and can also be included in navigation in-
structions. This might be more dicult for two reasons: First,
skylines and mountains usually look dierent from dierent
view angles, which is often not the case for buildings. This
results in larger training sets that are needed to determine their
visibility. Furthermore, in rural areas the availability of geo-
tagged images (e.g. by services like GSV) to determine the
visibility of the landmark could be limited as well. To overco-
me this, we could extend the pipeline to include approaches
that compute the visibility of landmarks in rural areas with
the help of digital elevation models (DEMs) [27] or to use
geotagged data from crowd-sourced approaches such as Open
Street View2.
This paper demonstrates that the Pharos approach is both
(1) feasible and (2) and has benefits compared to traditional
turn-by-turn (i.e. no landmark) instructions with a user study.
This means that global landmarks can be included in the na-
vigation instructions within cities for many routes, and that
global landmark-enriched instructions can be necessarily co-
gnizant of when the landmark is visible to a user and when it
is not.
Although we evaluated Pharos for pedestrian navigation in
urban areas, we are convinced that it can also be useful in
other contexts (e.g. outdoors while hiking) or in more rural
areas using mountains as global landmarks. For the future, we
also want to exploit the use of Pharos in rural environments.
Besides that, we are interested to explore whether Pharos
could be applied to other navigation domains, such as biking
or driving.
To achieve this we will further extend our pipeline to cover a
larger set of global landmarks and also include other global
landmarks that make sense for other modalities. Furthermore,
the visibility maps could also be used to calculate scenic routes
(e.g. for tourists) that guide the users through areas where
global landmarks are very often visible.
This work is supported by the Volkswagen Foundation through
a Lichtenberg professorship.
Note: This version of the paper contains a fix for a reference
issue that appeared in the original version.
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... Global landmarks are usually located far away from the observer or a specified route Elias & Paelke, 2008;Wenig et al., 2017). Therefore, their position relative to the observer is less affected by movement through space (Steck & Mallot, 2000). ...
... Therefore, their position relative to the observer is less affected by movement through space (Steck & Mallot, 2000). This allows to use global landmarks as indicators for world directions (Lin et al., 2012;Wenig et al., 2017). ...
... Global landmarks on the other hand are a special case. They are located offside the route and are only used to estimate cardinal directions (Steck & Mallot, 2000;Wenig et al., 2017). Information about cardinal directions allows to identify an approximate travel direction, but it is not sufficient to follow an exact route. ...
In the research of salience, contrast parameters of selected stimuli relative to surrounding stimuli are identified and evaluated to predict and direct visual attention. Based on these parameters, a distinction is made between visual, semantic and structural salience in the context of spatial cognition and the perception of stimuli in 3D space. The aim of this thesis is to investigate to what extent these salience categories are suitable for predicting and directing visual attention in 2D maps. Based on eye tracking data, parameters for determining the visual, structural and semantic salience in maps are identified. In addition, effects of the salience parameters on the generation of mental representations of space are investigated. Finally, differences and similarities of salience in 3D space and in 2D maps are described and implications for the design of task-oriented maps are explained.
... In a digital era, cognitive tasks during navigation are increasingly taken over by GPS-enabled mobile map displays as interfaces to navigation systems that provide automatic self-localization, route planning, and turn-by-turn instructions in real-time (Wenig et al., 2017). Assisted with such mobile map displays at their fingertips, navigators likely follow the route shown on their mobile screens passively, which may limit their active exploration in the environment (Clemenson et al., 2021). ...
... Until very recently, the field did not directly consider the background and training of the navigator, or the effects on spatial learning (Thrash et al., 2019;Li, 2020). More recently, attention has also been drawn to reduce the adverse effects of GPS-enabled navigation aids on spatial learning, e.g., by geographic information scientists (GIScientists) (Wenig et al., 2017), cognitive scientists (Ruginski et al., 2019), and map user interface (UI/UX) designers (Ricker and Roth, 2018;Thrash et al., 2019;Li, 2020;Fabrikant, 2022). Among the ideas proposed, the appropriate inclusion and display of landmarks on GPS-enabled mobile maps has gained particular traction among cartographers and navigation researchers in GIScience (Raubal and Winter, 2002;Duckham et al., 2010;Credé et al., 2020;Keil et al., 2020;Liu et al., 2022). ...
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The continuous assessment of pedestrians’ cognitive load during a naturalistic mobile map-assisted navigation task is challenging because of limited experimental control over stimulus presentation, human-map-interactions, and other participant responses. To overcome this challenge, the present study takes advantage of navigators’ spontaneous eye blinks during navigation to serve as event markers in continuously recorded electroencephalography (EEG) data to assess cognitive load in a mobile map-assisted navigation task. We examined if and how displaying different numbers of landmarks (3 vs. 5 vs. 7) on mobile maps along a given route would influence navigators’ cognitive load during navigation in virtual urban environments. Cognitive load was assessed by the peak amplitudes of the blink-related fronto-central N2 and parieto-occipital P3. Our results show increased parieto-occipital P3 amplitude indicating higher cognitive load in the 7-landmark condition, compared to showing 3 or 5 landmarks. Our prior research already demonstrated that participants acquire more spatial knowledge in the 5- and 7-landmark conditions compared to the 3-landmark condition. Together with the current study, we find that showing 5 landmarks, compared to 3 or 7 landmarks, improved spatial learning without overtaxing cognitive load during navigation in different urban environments. Our findings also indicate a possible cognitive load spillover effect during map-assisted wayfinding whereby cognitive load during map viewing might have affected cognitive load during goal-directed locomotion in the environment or vice versa. Our research demonstrates that users’ cognitive load and spatial learning should be considered together when designing the display of future navigation aids and that navigators’ eye blinks can serve as useful event makers to parse continuous human brain dynamics reflecting cognitive load in naturalistic settings.
... They mark a salient feature in the environment and serve as points of reference that allow for spatial orienting and structuring of the environment (Presson and Montello, 1988). Ample evidence has shown that landmarks facilitate wayfinding efficiency (Wenig et al., 2017;Yesiltepe et al., 2021) and spatial memory of environments (Credé et al., 2019;Ligonnière et al., 2021). ...
... Despite the long-standing literature on the importance of landmarks for human navigation (Richter and Winter, 2014), existing mobile navigation systems typically do not directly refer to landmarks when providing turn-by-turn wayfinding directions. The omission of landmarks in navigation systems could be one reason why navigation systems are often found to negatively affect spatial learning (Parush et al., 2007;Anacta et al., 2017;Wenig et al., 2017;Ligonnière et al., 2021). When guided by turn-by-turn directions, navigators tend to passively follow the given route shown on mobile maps and do not actively make spatial decisions (Fenech et al., 2010;Clemenson et al., 2021). ...
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The frequent use of GPS-based navigation assistance is found to negatively affect spatial learning. Displaying landmarks effectively while providing wayfinding instructions on such services could facilitate spatial learning because landmarks help navigators to structure and learn an environment by serving as cognitive anchors. However, simply adding landmarks on mobile maps may tax additional cognitive resources and thus adversely affect cognitive load in mobile map users during navigation. To address this potential issue, we set up the present study experimentally to investigate how the number of landmarks (i.e., 3 vs. 5 vs. 7 landmarks), displayed on a mobile map one at a time at intersections during turn-by-turn instructions, affects spatial learning, cognitive load, and visuospatial encoding during map consultation in a virtual urban environment. Spatial learning of the environment was measured using a landmark recognition test, a route direction test, and Judgements of Relative Directions (JRDs). Cognitive load and visuospatial encoding were assessed using electroencephalography (EEG) by analyzing power modulations in distinct frequency bands as well as peak amplitudes of event-related brain potentials (ERPs). Behavioral results demonstrate that landmark and route learning improve when the number of landmarks shown on a mobile map increases from three to five, but that there is no further benefit in spatial learning when depicting seven landmarks. EEG analyses show that relative theta power at fronto-central leads and P3 amplitudes at parieto-occipital leads increase in the seven-landmark condition compared to the three- and five-landmark conditions, likely indicating an increase in cognitive load in the seven-landmark condition. Visuospatial encoding indicated by greater theta ERS and alpha ERD at occipital leads with a greater number of landmarks on mobile maps. We conclude that the number of landmarks visualized when following a route can support spatial learning during map-assisted navigation but with a potential boundary—visualizing landmarks on maps benefits users’ spatial learning only when the number of visualized landmarks shown does not exceed users’ cognitive capacity. These results shed more light on neuronal correlates underlying cognitive load and visuospatial encoding during spatial learning in map-assisted navigation. Our findings also contribute to the design of neuro-adaptive landmark visualization for mobile navigation aids that aim to adapt to users’ cognitive load to optimize their spatial learning in real time.
... ( ) ( ) [20] [23] [21], [22] ( ) [ Table 1 Guidance information and presentation method used in pedestrian navigation systems using wrist-worn wearable device. ---------- [6], [8], [10] [6], [8], Table 2 Device type and guidance information on evaluation system. Fig. 3 Cumulative destination arrival rate relative to normalized travel time. ...
... ( ) ( ) [20] [23] [21], [22] ( ) [ Table 1 Guidance information and presentation method used in pedestrian navigation systems using wrist-worn wearable device. ---------- [6], [8], [10] [6], [8], Table 2 Device type and guidance information on evaluation system. Fig. 3 Cumulative destination arrival rate relative to normalized travel time. ...
This paper conducts a comparative evaluation experiment of pedestrian navigation systems including smartwatches in terms of both performance of navigation and safety. First, we construct three types of evaluation systems with different navigation information presented by smartwatches. Next, we conduct an evaluation experiment of the pedestrian navigation system with and without a smartwatch, and show that it is possible to achieve both performance of navigation and safety by using the smartwatch to present only the notifications, recommended directions, and supplementary information that most users of the pedestrian navigation system need. Finally, we perform an evaluation experiment of a pedestrian navigation system using a smartwatch that includes a screen display, and show that presenting a large amount of guidance information on the smartwatch degrades not only safety but also navigation performance. These results indicate that one of the important requirements for achieving both performance of navigation and safety in pedestrian navigation systems including smartwatches is to present only the information that is actually necessary for the user.
... Landmarks are actually rarely used in mainstream commercial systems due to the difficulty of getting updated data about them, and the ability to automatically select and integrate them in navigation devices. But a considerable number of recent works in the engineering sciences attempt to integrate landmarks into navigation assistant devices [11], by using spatial databases such as point of interest and GIS databases including road networks and cadastral maps [12], or dynamic sources such as web content, crowdsourcing data, and social networking websites [13]. These efforts are however focused on sighted people, and the issue could be even more delicate for visually impaired people if visual saliency is the primary quality used to recognize and use landmarks during navigation and wayfinding, and mostly if landmarks differed between sighted and visually impaired people. ...
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Navigating in urban environment is a major challenge for visually impaired people. Spatial landmarks are crucial for them to orient and navigate in their environment. In this paper, the spatial landmarks most important and commonly used by visually impaired people are identified through interviews, and geometric constraints of these landmarks are constructed to facilitate the development of map-matching algorithms. Interviews were conducted with 12 visually impaired people who had a range of visual impairments and used various mobility aids. Data were analyzed by sensory modality, occurrence of use, and number of users. 14 main landmarks for urban navigation were selected and categorized into two groups: Waypoints and Reassurance Points, depending on whether they are directly detected by touch. Geometric constraints were developed for each landmark to prepare their integration into map-matching or path-planning algorithms. The result is a comprehensive dictionary of landmarks and their geometric constraints is created, specifically tailored to help visually impaired people navigate urban environments. Our user-centric approach successfully translates the subjective navigation experiences of visually impaired people into an objective, universally accessible format. This bridges the gap between personal experiences and practical applications and paves the way for more inclusive navigation solutions for visually impaired people in urban environments.
... tourists [2] but often rely on complex object detection [30]. Global landmarks outperform classical navigational approaches as well and Wenig et al. [31] provided a general direction on a smartwatch. However, while global landmarks are very prominent, a different perceptions and relations are required for navigation especially the creation and usage of a mental map. ...
... This result also verified that landmarks were easier to perceive than other spatial objects and had obvious advantages [9,[43][44][45], and it is recommended that evacuation assembly points should be located close to the landmark, the landmark acts as a spatial reference point. This helps people locate, orient, and navigate faster in complex space environments during emergency evacuation [46][47][48][49][50]. ...
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An effective disaster prevention map ensures public safety and efficient evacuation during emergencies. Emergency evacuation information design in Taiwan is in its nascent stages. This study focuses on individuals' understanding, behavior, and decision-making during disaster prevention to devise suitable disaster prevention map norms. We examined the Emergency Planning Zone's existing disaster prevention methods and surveyed the community to understand their disaster prevention concepts and needs. We conducted two experiments: the first tested the comprehension of existing disaster prevention maps and identified their issues, while the second evaluated a redesigned map based on Experiment 1's findings. We discovered that all age groups agree on needing accurate, fast information and diverse evacuation route options. Experiment 1 revealed disproportionate assembly point icons on the existing map, leading to navigation difficulties. The map also failed to mark landmarks, road names, and blocked intersections accurately. The redesigned map in Experiment 2 addressed these issues, showing that improving map information design aids recognition and memory, and bridges wayfinding behavior gaps in people with different spatial abilities. We suggest marking evacuation routes on maps, placing corresponding signs on-site, and locating assembly points near landmarks for easier navigation.
... All of them, except [55,110], were targeting users without specific needs. Seven papers [21,43,46,55,71,113,115] considered including landmarks into the guidance information displayed for users. Nine of these studies considered the use of AR basing on the benefit of being able to receive the navigation guidance without altering the users' focus on their environment. ...
This doctoral thesis, in Human-Computer Interaction (HCI) field, addresses the question of mobility assistance for people with intellectual disability (ID). The main goal is to use a user-centred approach to design and evaluate a navigation aid system (NAS). Navigating independently through the environment is necessary to participate in different social activities and thus, it represents a key factor of social inclusion. Usually, people with ID encounter various navigation challenges, for instance: (1) Memorise a familiar path. (2) Remember and execute complex navigation instructions. (3) Poor adaptation when conditions change due to bus stop changes for example. In order to understand the human navigation behavior and different systems designed for this purpose, the state of the art was explored aiming to identify key elements to define the main research questions: What could be the contribution to improve human spatial navigation skills? How the spatial knowledge acquisition could be improved?How could an adapted navigation aid system be designed for people with ID? In this work, the pedestrian navigation behavior is explained using a detailed model of wayfinding, which is thecognitive process behind human navigation behavior. This model is designed to highlight people needs during the navigation task and offers an opportunity to design NAS to address them more efficiently. In addition, an ontology, about human navigation, was developed to define different key words used in this navigation field including the previous model. This ontology plays an important role in establishing a strong communication between different stakeholders that collaborate in this research field (researches in psychology, human-computer interaction, etc). Furthermore, new augmented reality (AR) glasses were tested to design a navigation aid system, and were compared to a smartphone. This comparison helped to investigate the effect of the device on navigation performance and memorisation of road landmarks. At the end, the designed system was adapted during co-design workshops gathering people with ID and professionals of the institution Udapei – IMPRO ( The system was tested considering two navigation tasks: (1) road crossing and (2) path following. Different aspects of road safety were considered during the co-design of the system. The obtained results are promising, and the users feel comfortable and confident about using the AR glasses on a daily basis when travelling to new destinations. This positive feedback is encouraging to upgrade the system and extend its scope to cover the entire spatial knowledge acquisition process (all states of the Wayfinding Model).
... This can be achieved by comparing the arrangement of surrounding spatial objects with their map representations (Kiefer et al. 2014). Especially the relevance of landmarks for self-localization, orientation and navigation has been emphasized by different authors (Anacta et al. 2017;Elias & Paelke 2008;Kiefer et al. 2014;Wenig et al. 2017). Due to their salience, landmarks are more likely to be perceived than other spatial objects (Röser 2017;Sorrows & Hirtle 1999). ...
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Map information, especially volunteered geographic information (VGI) is prone to spatial inaccuracies. Due to their use as spatial reference points, spatially inaccurate landmark representations in maps might affect the ability to match maps to the represented 3D space and might compromise self-localization and orientation. Based on a map matching task in a virtual 3D environment and various degrees of spatial landmark inaccuracies in a simultaneously presented 2D map, we aimed to identify acceptable and critical values of spatial inaccuracies. Furthermore, potential effects of inaccurate semantic spatial categories were evaluated. The findings demonstrate how metric and semantic spatial inaccuracies of landmark representations affect matching of maps to the represented 3D space. Map inaccuracies corresponding to more than 10 m within 3D space and swapped landmark pictograms in a map were associated with the perception of a mismatch between maps and 3D spaces. Furthermore, the distance of landmarks to the perceiver was found to affect map matching. Spatial inaccuracies of landmark pictograms were less likely to be associated with a perceived mismatch between maps and 3D spaces when the landmarks were further away, presumably because small or moderate inaccuracies were more difficult to perceive. To reduce the identified effects of landmark inaccuracies in maps on map-matching performance, we propose to identify means to quantify the uncertainty concerning spatial landmark inaccuracies and to visually communicate this uncertainty to map users.
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Smartphone map-based pedestrian navigation is known to have a negative effect on the long-term acquisition of spatial knowledge and memorisation of landmarks. Landmark-based navigation has been proposed as an approach that can overcome such limitations. In this work, we investigate how different interaction technologies, namely smartphones and augmented reality (AR) glasses, can affect the acquisition of spatial knowledge when used to support landmark-based pedestrian navigation. We conducted a study involving 20 participants, using smartphones or augmented reality glasses for pedestrian navigation. We studied the effects of these systems on landmark memorisation and spatial knowledge acquisition over a period of time. Our results show statistically significant differences in spatial knowledge acquisition between the two technologies, with the augmented reality glasses enabling better memorisation of landmarks and paths.
Conference Paper
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Providing pedestrian navigation instructions on small screens is a challenging task due to limited screen space. As image-based approaches for navigation have been successfully proven to outperform map-based navigation on mobile devices, we propose to bring image-based navigation to smartwatches. We contribute a straightforward pipeline to easily create image-based indoor navigation instructions that allow users to freely navigate in indoor environments without any localization infrastructure and with minimal user input on the smartwatch. In a user study, we show that our approach outperforms the current state-of-the art application in terms of task completion time, perceived task load and perceived usability. In addition, we did not find an indication that there is a need to provide explicit directional instructions for image-based navigation on small screens.
Conference Paper
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Pedestrian navigation systems require users to perceive, interpret, and react to navigation information. This can tax cognition as navigation information competes with information from the real world. We propose actuated navigation, a new kind of pedestrian navigation in which the user does not need to attend to the navigation task at all. An actuation signal is directly sent to the human motor system to influence walking direction. To achieve this goal we stimulate the sartorius muscle using electrical muscle stimulation. The rotation occurs during the swing phase of the leg and can easily be counteracted. The user therefore stays in control. We discuss the properties of actuated navigation and present a lab study on identifying basic parameters of the technique as well as an outdoor study in a park. The results show that our approach changes a user's walking direction by about 16°/m on average and that the system can successfully steer users in a park with crowded areas, distractions, obstacles, and uneven ground.
Conference Paper
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Map applications for smartwatches present new challenges in cartography, a domain in which large display sizes have significant advantages. In this paper, we introduce StripeMaps, a system that adapts the mobile web design technique of linearization for displaying maps on the small screens of smartwatches. Just as web designers simplify multiple column desktop websites into a single column for easier navigation on mobile devices, StripeMaps transforms any two-dimensional route map into a one-dimensional "stripe". Through a user study, we show that this simplification allows StripeMaps to outperform both traditional mobile map interfaces and turn-by-turn directions for pedestrian navigation using smartwatches. In addition to introducing StripeMaps, this paper also has a secondary contribution. It contains the first empirical comparison of different approaches for pedestrian smartwatch navigation and illuminates their pros and cons.
Conference Paper
Research examining pedestrian navigation systems that use landmarks to explain routes became popular in the past years. Nevertheless, it is still an open question how many landmarks should be depicted at once. In this paper a user study is presented that evaluates two different indoor navigation system designs that depict either one (N = 63) or four (N = 60) landmarks to guide the user. The time it took the participants to recognize where to go was captured as a dependent variable. Results show that the interface only depicting one landmark leads to faster self-localization. Therefore, it is argued that a pedestrian navigation system should mainly depict one highly salient landmark in a navigation instruction in order to keep navigation efficiency high.
Conference Paper
Context awareness is crucial for ubiquitous computing, and position is an important aspect of context. In an ideal world, every stationary object or entity in the built environment would be associated with position, so that applications can have precise spatial context about the environment surrounding a human. In this paper, we take a step towards this ideal: by analyzing images from Google Street View that cover different perspectives of a given object and triangulating the location of the object, our system, ALPS, can discover and localize common landmarks at the scale of a city accurately and with high coverage. ALPS contains several novel techniques that help improve the accuracy, coverage, and scalability of localization. Evaluations of ALPS on many cities in the United States show that it can localize storefronts with a coverage higher than 90% and a median error of 5 meters.
Conference Paper
Navigation in unfamiliar cities often requires frequent map checking, which is troublesome for wayfinders. We propose a novel approach for improving real-world navigation by generating short, memorable and intuitive routes. To do so we detect useful landmarks for effective route navigation. This is done by exploiting not only geographic data but also crowd footprints in Social Network Services (SNS) and Location Based Social Networks (LBSN). Specifically, we detect point, area, and line landmarks by using three indicators to measure landmark's utility: visit popularity, direct visibility, and indirect visibility. We then construct an effective route graph based on the extracted landmarks, which facilitates optimal path search. In the experiments, we show that landmark-based routes out-perform the ones created by baseline from the perspectives of the lap time and the number of references necessary to check self-positions for adjusting route directions.
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In this paper, we investigate how people with mobility impairments assess and evaluate accessibility in the built environment and the role of current and emerging location-based technologies therein. We conducted a three-part formative study with 20 mobility impaired participants: a semi-structured interview (Part 1), a participatory design activity (Part 2), and a design probe activity (Part 3). Part 2 and 3 actively engaged our participants in exploring and designing the future of what we call assistive location-based technologies (ALTs) location-based technologies that specifically incorporate accessibility features to support navigating, searching, and exploring the physical world. Our Part 1 findings highlight how existing mapping tools provide accessibility benefits even though often not explicitly designed for such uses. Findings in Part 2 and 3 help identify and uncover useful features of future ALTs. In particular, we synthesize 10 key features and 6 key data qualities. We conclude with ALT design recommendations.
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Vi-Bros is a new interface that simultaneously utilizes two mobile devices, a smartphone and a smartwatch, to provide users with intuitive guidance during indoor navigation. We evaluate the validity of the dual device interaction with the tactile feedback and provide insights for user experience via two experiments; one from a controlled environment and the other from the field. We present the core insights and potential design space for developing multi-device interaction.