Geo-localization using Ridgeline Features Extracted from
360-degree Images of Sand Dunes
Shogo Fukuda1 , Shintaro Nakatani2 , Masashi Nishiyama2 and Yoshio Iwai2
1 Graduate School of Sustainability Science, Tottori University, Tottori, Japan
2 Graduate School of Engineering, Tottori University, Tottori, Japannishiyama@tottori-u.ac.jp

Keywords:

Geo-localization, ridgelines, 360-degree images, sand dune

Abstract:

We propose a method to extract the features of sand-dune ridgelines using a 360-degree camera to improve
the accuracy of estimating geo-locations. It is difficult to estimate geo-locations in an outdoor environment
with almost no texture such as in sand dunes. We focus on the feature of the ridgeline, which is the boundary
between the ground region and the sky region. A 360-degree camera can quickly detect the ridgeline signal
in all directions in a sand dune. Our method determines the current location by searching for the nearest
ridgeline signal from target signals and pairing with their geo-locations. We evaluated the accuracy of thisgeo-localization method using synthesized images generated from a digital elevation model. We also evaluated it using real 360-degree images collected in sand dunes. We confirmed that our method significantly
outperformed the existing geo-localization method on both synthesized and real images.

1

INTRODUCTION

Geo-localization systems have attracted attention
to increase the use of tourism resources. A geolocalization system can guide users to specific locations and provide digital content suitable for each location. To guide users accurately, there is a need fora technique to estimate the geo-location of a user. In
this paper, we discuss how to estimate geo-locationsin an outdoor environment that is nearly textureless.
We consider sand dunes, which are a noted tourist resource of Tottori prefecture in Japan. In sand dunes,
simple and similar textures are widely spread. Thus,
we have to carefully consider how to accurately estimate the geo-locations.
Many existing methods for estimating geolocations have been proposed. Here, we classify
the existing methods into three types. In the first
type (Li et al., 2015; Hofmann-Wellenhof et al., 2001;
Groves, 2013), the location is acquired using a GNSS
(Global Navigation Satellite System) such as GPS or
GLONASS. The existing methods have a problem inthat the accuracy of the geo-location estimation depends on the arrangement and number of satellites
that are observed from the location of the user. In
the second type (Kim et al., 2017; Brejcha and Čadı́k,
2017; Lowry et al., 2016), the geo-location is estimated using texture patterns in the images acquired

from a camera. We must consider that outliers frequently occur when simple and similar textures repeat
in a sand dune environment. In the third type (Piasco
et al., 2018; Chen et al., 2018; Dusha and Mejias,2012), the accuracy of estimating the geo-location is
improved using both GNSS and the camera. These
existing methods set the initial location using GNSS
and determine the current location using the image
acquired from the camera. These methods should improve the accuracy compared with the methods thatuse only GNSS or a camera. However, the difficult
problem of little texture still remains in sand dune environments.
Our aim is to improve the accuracy of geo-location
estimation in a nearly textureless outdoor environment. We investigate robust features that can be detected from the images of sand dunes. The images include two regions, the ground and the sky. The groundregion is covered with sand, so it is unsuitable for feature extraction because there is little texture. The sky
region contains many objects, such as clouds and thesun, that disturb the feature extraction because thesealways move over time. Hence, it is difficult to use either region to improve the accuracy of estimating geolocation. In this paper, we focus on the feature of theridgeline, which is the boundary between the groundregion and the sky region. Existing methods (Saurer
et al., 2016; Baatz et al., 2012; Nicolle et al., 2017;

Porzi et al., 2016) have introduced ridgelines that arerobust to changes in vegetation, illumination, and season in the outdoor environment. We assume that theridgelines work effectively even in a nearly textureless environment such as sand dunes. Note that theexisting methods are designed using a general camera with a limited angle of view to extract the features. We need to consider that ridgelines spread toall 360-degree directions in sand dunes. It cannot be
said that the existing methods are sufficient to represent the features of the ridgelines.
In this paper, we propose a method to extract thefeatures of the ridgelines using a 360-degree camera. Our aim is to improve the accuracy of geolocalization in sand dunes. The 360-degree camera
can quickly acquire an image containing all angles ofthe view and can easily detect the ridgelines of all directions. We regard the ridgelines as continuous signals and extract their amplitude components as features. Our method sets the search range of the geolocation using the initial location obtained by GNSS
and determines the current location by searching forthe nearest ridgeline feature. To confirm the effectiveness of our method, we evaluated the accuracy of
the geo-location estimation using synthesized images
generated from a digital elevation model. Furthermore, we collected the 360-degree images of a sanddune environment and confirmed that our method improves accuracy.
This paper is organized as follows. Section 2 describes the details of our method, Section 3 presents
the results of a preliminary experiment using GNSS,
Section 4 describes the results of geo-localization using the synthesized images, and Section 5 describes
the result of the evaluation using real 360-degree images. Our concluding remarks are given in Section 6.

2

Estimation of geo-location using
ridgeline signals

2.1

Ridgelines in a sand dune
environment

Ridgeline

(a)

(b)

(c)

Figure 1: Example of a ridgeline detected from a 360degree image of a sand dune environment (a). Two 360degree images acquired under different weather conditions(b) and (c).

can use the ridgeline signals as features when estimating geo-location. We discuss the characteristicsof the ridgelines below. A sand dune does not change
its shape rapidly over time because of coastal conservation activities. There are no obstacles such asbuildings at most locations on the sand dunes. Furthermore, it is difficult to see the buildings that standseveral kilometers away because of the height difference in the sand dunes. We thus can stably observe
the parallax included in the ridgeline signals in all directions. There is also an advantage in that the ridgelines can be easily detected from 360-degree images
because the textures of the ground and sky regions arevery different. Figures 1(b) and (c) show examplesof 360-degree images acquired from the sand dunes.
Given the above characteristics, we believe that geolocalization using ridgeline signals works effectively
in sand dune environments.

2.2
A ridgeline generally refers to a narrow area of highland along the top of a line of hills. In this paper, the ridgeline refers to the boundary between the
ground region and the sky region in the image acquired from a 360-degree camera. Figure 1(a) shows
an example of a ridgeline. We represent a ridgeline asa one-dimensional waveform signal. When the user
moves, parallax occurs between the ridgeline signals
observed before and after movement. Therefore, we

Overview of our method

Figure 2 illustrates an overview of our method. In
step S1, we generate a target database to store thepairs of ridgeline signals and their geo-locations. The
system collects a 360-degree image at each locationin the sand dune area and detects the ridgeline signal in advance. Steps S2 to S4 are used to estimate
the geo-location of the user. In step S2, the user acquires a query 360-degree image using the camera

S1: Store target ridgeline
signal andgeo-locations

Location 1
Location N
Target ridgeline signals(a)

S2: Obtain an initial location anda query 360-degree image
S3: Detect the query ridgeline signal
S4: Search for the nearest target ridgeline
signal using the PSD features(b)

Geo-location

Figure 2: Overview of our method. We set the search rangeof the geo-location using the initial location obtained by
GNSS and detect the current position by searching for thenearest ridgeline signal.

and his or her initial location using GNSS. In step S3,
our method detects the ridgeline signal from the 360degree image. We divide the 360-degree image into
the sky and ground regions using color distributions,and determine the region boundary to detect the ridgeline signal. In step S4, we determine the geo-location
by searching for the target ridgeline signal that is mostsimilar to the query ridgeline signal. It is not practical to search through all ridgeline signals contained in
the target database. Instead, our method searches only
the neighborhood of the initial location acquired from
GNSS by limiting the search range in the database.
Our aim is to prevent outliers and reduce processing
time. Our method determines the search range using
the error of the initial location (see Section 3). The
search for ridgeline signals is described in detail below.

2.3

Searching for the nearest target
ridgeline signals using PowerSpectrum Density (PSD) features

Our method searches for the target ridgeline signalthat has the waveform that is most similar to the given
query ridgeline signal. To accurately search for the
ridgeline signal, it is necessary to consider the variation of camera poses when acquiring the 360-degree
images. Recently available 360-degree cameras havevarious tilt correction functions, which suppresses thevariation in attitude. To more stably extract features from ridgeline signals, our method uses the PSD(Power Spectrum Density). The PSD is a frequencybased analysis and is represented by the power spectrum components for each frequency band. The frequency analysis assumes that the query signal is a

(c)

(d)

Figure 3: Examples of 360-degree images acquired from areal camera (a) and (c). Synthesized images generated fromthe digital elevation model (b) and (d).

continuous waveform, so it has a high affinity with theridgeline signal acquired from a 360-degree image.
The PSD also has the advantage of not being affectedby phase shift. Note that we remove the componentsof the frequency band above fmax because high frequency components sometimes contain noise when
detecting ridgeline signals. Our method determines
the target ridgeline signal corresponding to the queryridgeline signal when the L1 norm between the two
PSD features is the smallest. Our method outputs the
final geo-location linked to the target ridgeline signal.

3

Preliminary experiment: the errorof the initial location obtained by
GNSS

Our method uses GNSS to set the search range
for the target ridgeline signals from the initial location. To determine this range, we investigated
how much error occurs in commonly available GNSS
terminals. We used a mobile terminal (GARMIN:
GPSMAP64SJ) that records the current position using
GPS, GLONASS, and QZSS. We measured known
location points managed by the Geospatial Informa-tion Authority of Japan, which are generally used
for map making and various topographical surveys.
We acquired the current values of the latitude andlongitude using the mobile terminal at each known
point and compared them with the correct latitude andlongitude values of the known point. We computed
the error in distance between the current estimatedpoint and the known point using these values. Threeknown points (L010000001070, TR35334212403,
and TR45334214401) were used. We performed oneminute measurements three times on sunny days. The
results of the evaluation show that the estimation error of the initial location using GNSS is 2.5 ± 1.3 m.
In the following experiments, we set the search rangeof the target ridgeline signals to a radius of 5 m fromthe initial location, which is a margin of 2σ of the estimation error for the search range.
In recent years, RTK-GPS (Langley, 1998; Odijk
et al., 2017) has attracted attention because it is ahighly accurate GNSS and a low-cost device. The
RTK-GPS system uses a reference station on the
ground and corrects the estimated locations using positioning satellites. We set the reference station lessthan 5 km of the known points. Here, we evaluatedthe estimation error of the current locations using thesingle-band RTK-GPS (NEO-M8T, u-blox). We confirmed that the estimation error was within several
tens of centimeters when the RTK-GPS system obtained fixed solutions. However, it was necessary to
wait for several tens of seconds while standing thesame location to obtain the fixed solution. Furthermore, there were some cases where the fixed solution
could not be obtained even after waiting a long time.
We believe that the use of RTK-GPS is still premature.
We thus used the estimation error computed from thecommonly available GNSS terminal to determine thesearch range of the target ridgeline signal.

4

Basic performance of
geo-localization evaluated using
the synthesized images

4.1

Dataset generated from the digital
elevation model

We generated synthesized images of the ridgeline signals using the digital elevation model (hyo, 2 12)
provided by the Geospatial Information Authority ofJapan. The model contains three-dimensional meshes
at intervals of 5 m. We generated the synthesized images of the ridgelines using graphics rendering engine (Unity). Figures 3(a) and (c) show real 360-

25 m
25 m
RegionDigital Elevation Model (5 mmesh)Evaluation point

Figure 4: Evaluation points on the digital elevation modelof sand dunes.

Initial location (𝑥 ", 𝑦" )
1m

1m
5m

Target:Radius of 5 m in intervals of 1 m frominitial location (𝑥 " ,𝑦" )Test:
Location 𝑥(, 𝑦( = (𝑥 " + ε, , 𝑦" +ε- )ε, , ε- : [−0.5, 0.5] uniform random noise
Location of the target ridgeline signal
Range of the query ridgeline signal

Figure 5: Locations used to generate the synthesized images
at certain evaluation points.

degree images, and Figs. 3(b) and (d) show synthesized images assuming the same location as the real
images. When generating the synthesized images, werendered the ground regions as black and the sky regions as white. We generated a 360-degree image by
connecting the center pixel rows of candidate images.
We made a candidate image of each orientation by rotating a camera with a field angle of θ by A. The parameter settings θ = 100 degrees and A = 0.5 degrees
were adjusted by comparing the synthesized image
with the waveform of the actual 360-degree image.
The resolution of the synthesized image was set to
720 × 180 pixels.
We used an area of a 500 × 500 m square to generate the synthesized images. Figure 4 shows the evaluation points on the digital elevation model. We set
20 × 20 = 400 evaluation points on a 25-m grid. At
each evaluation point, synthesized images for the target and query were generated. Figure 5 shows thelocations used to generate the synthesized images ata certain evaluation point. In the 5 m radius from an
initial position (xc , yc ), which represents the center ofthe search range described in Section 2.3, 73 synthesized target images were generated at locations in intervals of 1 m. The 10 synthesized query images weregenerated at an initial location (x̂, ŷ) with uniform random noise (εx , εy ) in the range [−0.5, 0.5]. The heightof the camera from the ground surface was fixed at aheight of 100 cm.

Table 1: Average errors of the geo-localization evaluatedusing the synthesized images of the digital elevation model.

Existing method
Baseline
Our method

4.2

Average error [m]0.79 ± 0.95
0.60 ± 0.75
0.42 ± 0.60

Results for synthesized images

(a)

(b)

(c)

(d)

We compared the accuracies of several geolocalization methods to confirm the effectiveness of
our method by evaluating them using the synthesizedimages. We extracted features using the following
methods:• Existing method: We extracted the histogram
features of the ridgelines for geo-localization using the existing method (Saurer et al., 2016).
The ridgeline signals were divided into small segments. The distributions of gradients of contours
in the segments were computed to generate thecontour words, which are similar in spirit to visual
words. We set the number of the contour words to
32.

Average error [m] 0

3

Figure 6: Visualization of the average errors at all evaluation points. (a) The three-dimensional shape of the all evaluation points when viewed from above. The average errorsof the (b) existing method, (c) baseline, and (d) our method.

• Baseline: We used the fast Fourier transform(FFT) to extract features from the ridgeline signals. The FFT is a representative approach of
frequency analysis. We used the amplitude components as the feature, and set the maximum frequency band of 15 Hz to increase the accuracy.

errors for many evaluation points. In contrast, we
confirmed that our method (Fig. 6(d)) was able to estimate the geo-location at 1 m or less at most evaluation
points, although the average errors were still large atsome points. We discuss the reason for these large
errors below.

• Our method: We used the PSD features of theridgeline signals. The maximum frequency band
was fmax = 15.

4.3

We used the 360-degree images of the ridgelines synthesized in Section 4.1. All experimental conditions
except for the features were the same for all threemethods. We computed the Euclidean distance fromthe ground-truth location to the estimated location ateach evaluation point. The errors of the geo-locations
were calculated at all evaluation points and their averages are reported.
Table 1 shows the average errors of the geolocalization evaluated using the synthesized imagesof the digital elevation model. We confirmed that
our method obtained higher accuracy than the existing
method and the baseline. We visualized the average
errors of the evaluation points with a heatmap. Figure 6(a) shows the area including all evaluation points
on the digital elevation model. In Figs. 6(b), (c), and(d), the heatmaps of the average errors for the existing method, the baseline, and our method are represented, respectively. We see that the existing method(Fig. 6(b)) and the baseline (Fig. 6(c)) yielded large

We investigated the locations at which the accuracy of
the geo-localization decreases. We hypothesized thatthe errors are large when the ridgeline signals contain deep depth values and the parallax is very small.
We generated histograms of the depth values of therendered ridgeline signals for evaluation points withgood accuracy and poor accuracy and compared them.
Figure 7 shows the distribution of depth values on the
top-10 and bottom-10 ridgelines with respect to evaluation point accuracy. We see that the distributions for
low-accuracy points tend to have deeper depth values
on the ridgeline signals than the distributions for highaccuracy points. In fact, we found that there are many
low-accuracy evaluation points on the top of a sanddune. We believe that the top of a sand dune has agood view and distant mountains are observed, so thechange in parallax of the ridgeline signals decreases.

Discussion of the errors of
geo-localization using our method

Table 2: Average errors of geo-localization evaluated using
real 360-degree images collected in a sand dune environment.

3,000

0

(0, 250)(250, 500) (500, 750)

(750,∞]

0(b)Frequency

Frequency

9

800

6,000

0

0

1
2
3
4
Average error (m)
(a)

5

0

1
2
3
4
Average error (m)
(b)

5

0

1
2
3
4
Average error (m)
(c)

5

3,000

9
0

Depth (m)Average error [m]3.62 ± 1.84
2.42 ± 1.72
1.81 ± 1.44

(0, 250)(250, 500] (500, 750) (750, ∞]Depth of the ridgeline (m)
(c)

0
800
0

(d)

Figure 7: Distribution of depths on ridgelines and depth
maps. (a) and (c) Depth distribution of the 10 most andleast accurate evaluation points. (b) and (d) Depth maps ofthe most and least accurate evaluation points.

A

9Frequency

Depth (m)Depth of the ridgeline (m)(a)

Existing method
Baseline
Our method

Frequency

Frequency

6,000

0

Figure 9: Distributions of the errors of the geo-localization
evaluated using the real 360-degree images. Results for the(a) existing method, (b) baseline, and (c) our method.
B

C

Figure 8: Examples of real 360-degree images. The target
and query images were acquired from each area of the sanddunes.

5

Evaluation using real 360-degree
images

We evaluated the accuracy of our method using
actual 360-degree images collected in the sand dune
environment. We used piles managed by Tottori prefecture to determine the locations where the 360degree images were acquired. The piles were struck

in a grid every 100 m. We defined three areas, eachcentered on a pile, and collected the 360-degree images in each area. Figure 8 shows examples of real
360-degree images, taken using a 360-degree camera (RICOH THETA m15) and a tripod. To generate a target database of ridgeline signals, we collected
19×3 = 57 target images per meter in the range of ±9
m to the north starting from each pile. We collected
9 × 3 = 27 query images per meter in the range of±4 m to the north. The resolution of the 360-degree
image was set to 720 × 180 pixels. We manually detected the ridgeline signals using a graphics editor(Adobe Photoshop). The search range of the target
database was set to ±5 m. The ridgeline signal feature extraction process was the same as that describedin Section 4.2.
Table 2 shows the average errors of the geolocalization obtained using real 360-degree images.

The results show that our method obtains a smaller
average error (less than 2 m) than the existing method
and baseline.
We investigated how much the error was distributed for each feature. Figure 9 shows the distributions of the errors of the geo-localization using thereal 360-degree images. In the results of the existing
method (Fig. 9(a)) and baseline (Fig. 9(b)), the peaksof the distributions have an error of 4 m. In contrast,
in our method (Fig. 9(c)), almost all cases had errors
less than 1 m, though there were a few cases with errors of 5 m. The above results confirm that the proposed PSD features extracted from the ridgeline signals are effective for geo-localization.

6

Conclusions

We proposed a method for estimating geo-location
using ridgeline features extracted from 360-degree
images. We evaluated the accuracy of the proposed
geo-localization method using synthesized images
generated from a digital elevation model. We confirmed that our method substantially outperformed
the existing method. Furthermore, we conducted anexperiment to evaluate geo-location using real 360degree images collected in the sand dunes. We confirmed that the average error of our method was lessthan 2 m.
In future work, we will further evaluate ourmethod on various datasets of with low texture using 360-degree cameras. We will also explore the useof synthesized images to create a target database to
reduce the cost of database generation.

REFERENCES(Reference 2019-2-12). Digital Map five meter Grid (Elevation), Geospatial Information Authority of Japan.
Baatz, G., Saurer, O., K. Köser, K., and Pollefeys, M.
(2012). Large scale visual geo-localization of images
in mountainous terrain. In Proceedings of the European Conference on Computer Vision, pages 517–530.
Brejcha, J. and Čadı́k, M. (2017). State-of-the-art in visual
geo-localization. Pattern Analysis and Applications,20(3):613–637.
Chen, X., Hu, W., Zhang, L., Shi, Z., and Li, M. (2018).
Integration of low-cost gnss and monocular cameras
for simultaneous localization and mapping. Sensors
18(7) 2193.
Dusha, D. and Mejias, L. (2012). Error analysis and attitude observability of a monocular gps/visual odometry integrated navigation filter. International Journalof Robotics Research, pages 1–41.

Groves, P. D. (2013). Principles of gnss, inertial, and multisensor integrated navigation systems. Artech House.
Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J.
(2001). Global positioning system theory and practice. Springer-Verlag Wien.
Kim, H. J., Dunn, E., and Frahm, J. (2017). Learned contextual feature reweighting for image geo-localization.
In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), pages 3251–
3260.
Langley, R. B. (1998). Rtk gps. In GPS WORLD, pages
70–76.
Li, X., Zhang, X., Ren, X., Fritsche, M., Wickert, J.,
and Schuh, H. (2015). Precise positioning with current multi-constellation global navigation satellite systems: Gps, glonass, galileo and beidou. Scientific Reports, 5(8328):1–14.
Lowry, S., Sunderhauf, N., Newman, P., Leonard, J. J.,
Cox, D., Corke, P., and Milford, M. J. (2016). Visual place recognition: A survey. IEEE Transactions
on Robotics, 32(1):1–19.
Nicolle, L., Bonneton, J., Konik, H., Muselet, D., andTougne, L. (2017). Towards an electronic orientationtable: using features extracted from the image to register digital elevation model. In Proceedings of theInternational Conference on Computer Vision Theory
and Applications, pages 28–38.
Odijk, D., Nadarajah, N., Zaminpardaz, S., and Teunissen,
P. J. G. (2017). Gps, galileo, qzss and irnss differential isbs: estimation and application. GPS Solutions,21(2):439–450.
Piasco, N., Sidibe, D., Demonceaux, C., and Gouet-Brunet,
V. (2018). A survey on visual-based localization: On
the benefit of heterogeneous data. Pattern Recognition, 74:90 – 109.
Porzi, L., Rota, B. S., and Ricci, E. (2016). A deeplysupervised deconvolutional network for horizon line
detection. In Proceedings of the 24th ACM International Conference on Multimedia, pages 137–141.
Saurer, O., Baatz, G., Köser, K., Ladický, L., and Pollefeys, M. (2016). Image based geo-localization in
the alps. International Journal of Computer Vision,
116(3):213–225.