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Aerial perspective for shaded relief

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Aerial perspective is an essential design principle for shaded relief that emphasizes high elevation terrain using strong luminance contrast and low elevations with low contrast. Aerial perspective results in a more expressive shaded relief and helps the reader to understand the structure of a landscape more easily. We introduce a simple yet effective method for adding aerial perspective to shaded relief that is easy to control by the mapmaker.
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Aerial perspective for shaded relief
Bernhard Jenny & Tom Patterson
To cite this article: Bernhard Jenny & Tom Patterson (2020): Aerial perspective for shaded relief,
Cartography and Geographic Information Science
To link to this article: https://doi.org/10.1080/15230406.2020.1813052
Published online: 07 Oct 2020.
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Aerial perspective for shaded relief
Bernhard Jenny
a
and Tom Patterson
b
a
Faculty of Information Technology, Monash University, Melbourne, Australia;
b
U.S. National Park Service (Retired), Harpers Ferry, WV, USA
ABSTRACT
Aerial perspective is an essential design principle for shaded relief that emphasizes high elevation
terrain using strong luminance contrast and low elevations with low contrast. Aerial perspective
results in a more expressive shaded relief and helps the reader to understand the structure of
a landscape more easily. We introduce a simple yet eective method for adding aerial perspective
to shaded relief that is easy to control by the mapmaker.
ARTICLE HISTORY
Received 26 May 2020
Accepted 18 August 2020
KEYWORDS
Swiss style relief shading;
hillshade; shaded relief;
aerial perspective; proximity
luminance covariance;
atmospheric depth
1. Introduction
Through the use of illuminating and shadowing, shaded
relief images show terrain as a continuous, three-
dimensional surface on a flat map that is easy to under-
stand for most map readers. Cartographers have devel-
oped a series of design principles for creating effective
shaded relief images (Imhof, 1982). These design prin-
ciples include using an illumination direction from the
upper left to avoid the illusion of terrain inversion
(Biland & Çöltekin, 2017), locally adjusting illumination
to accentuate the shape of individual landforms (Brassel,
1974a; Veronesi & Hurni, 2014, 2015), adjusting bright-
ness of major landforms (Kennelly & Stewart, 2006;
Marston & Jenny, 2015), showing flat areas with
a consistent gray tone (Jenny, 2001), and using aerial
perspective to more clearly show the vertical distribu-
tion of elevation (Brassel, 1974a; Imhof, 1982; Patterson,
2001a). As observed in nature, aerial perspective, also
known as atmospheric perspective, is due to haze and
other particles in the atmosphere that scatter light. As
a result, landscape features further away in the back-
ground appear fainter than those in the foreground. The
contrast in luminance and color between foreground
and background features decreases with distance from
the viewer this is a technique favored by classic pain-
ters for adding depth to landscape paintings.
Cartographers use aerial perspective in shaded relief as
a visual cue to differentiate between high mountain
summits and lowlands (Figure 1). When a map reader
observes the terrain from above, mountains appear clo-
ser to the reader so there is a stronger contrast between
dark, shaded slopes and bright, illuminated slopes. More
distant topography, such as lowlands, is shown with
reduced contrast. The result is a shaded relief image
that emphasizes the three-dimensional effect and that
clearly distinguishes between high and low elevations.
Our contribution is a simple algorithm for adding
aerial perspective to grayscale shaded relief images.
When developing this method, we had the following
goals: (1) the user should be able to easily control the
application through a minimum number of parameters
that are simple to understand; (2) the algorithm should
be simple to add to existing shading algorithms; (3)
adding aerial perspective should not change the gray
value of flat areas so as to maintain a uniform base
color from which the terrain features rise up from or
fall below; and (4) the method must be flexible in order
to accommodate various shading methods, for example,
methods that combine multiple illumination directions
(e.g., Mark, 1992), locally adjust illumination direction
to terrain features (Brassel, 1974a), or use neural net-
works for transferring shading from manual to digital
shaded reliefs (Jenny et al., 2020). It is to note that our
method is not a physical model of atmospheric scatter-
ing, but an attempt at replicating the design principles of
aerial perspective as used in manual relief shading
(Imhof, 1982).
2. Related work
In perceptual science, aerial perspective is also called
proximity luminance covariance (Schwartz & Sperling,
1983). Variations in both luminance (Dosher et al.,
1986) and saturation (Dresp-Langley & Reeves, 2014)
are effective depth cues for the human visual system
CONTACT Bernhard Jenny bernie.jenny@monash.edu
CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE
https://doi.org/10.1080/15230406.2020.1813052
© 2020 Cartography and Geographic Information Society
(Ware, 2019), while variation in hue has a weak effect on
perceived distance (Egusa, 1983; Guibal & Dresp, 2004).
While varying color based on elevation and slope expo-
sure to illumination can emphasize aerial perspective
(Imhof, 1982; Jenny & Hurni, 2006; Patterson, 2001b;
Tait, 2002), we focus on aerial perspective for mono-
chromatic grayscale shading in this paper and adjust
luminance.
Luminance is the main input for shape-from-shading,
the visual system’s ability to extract a three-dimensional
shape from at flat shading. It has been demonstrated
that the human visual system assumes light shines from
above (Kleffner & Ramachandran 1992) or above-left
(Gerardin et al., 2007; Sun & Perona, 1998). This expec-
tation of light from above also causes the relief inversion
eect in shaded relief, where mountains are perceived as
valleys and valleys as mountains when illuminated from
below (Bernabé-Poveda & Çöltekin, 2015; Biland &
Çöltekin, 2017; Çöltekin & Biland, 2019).
In computer graphics, aerial perspective is known as
atmospheric depth and is commonly used to convey
depth in computer-generated scenes. Luminance and
saturation are varied with the distance to the observer
(Tai & Inanici, 2012). The atmospheric depth in land-
scape photographs can also be adjusted with computer
graphics algorithms. For example, Zhang et al. (2014)
propose a method inspired by landscape paintings that
adjusts local contrast to increase the illusion of depth in
landscape photographs.
In cartography, aerial perspective is a key concept in
Imhof’s (1982) seminal work on terrain mapping for
both grayscale and colored relief shading. Imhof identi-
fies the following benefits of aerial perspective: “It
increases the three-dimensional effect, supports the
inter-relationship of generalized forms and prevents
the optical illusion of relief inversion.” Imhof also
warns that aerial perspective should only be “introduced
when there are considerable differences in elevation,
and even then with great discretion” (Imhof, 1982,
p. 173). Excessively diminishing contrast at lower eleva-
tions could flatten those areas and misrepresent the
actual character of the terrain.
In digital cartography, Yoeli’s pioneering work on ana-
lytical relief shading with digital elevation models (Yoëli,
1965; Yoëli, 1966) was soon followed by Brassel’s (1974a)
suggestion to simulate aerial perspective with a digital
algorithm. Brassel’s method increases the luminance con-
trast of pixels if they are above the mean elevation of the
model and decreases the contrast of pixels below the mean
elevation. Brassel’s method does not change the gray value
of flat areas. For each pixel, the difference between its gray
value g and the gray value of flat areas gf is computed first.
This difference is then multiplied by an elevation-
dependent scale factor, and the gray value of flat areas gf
is added to compute g0, the gray value with aerial perspec-
tive (Equation (1) Erratum: we found an error in the
equation in Brassel’s English language paper (Brassel,
1974a), which is corrected in a notice in this issue (Brassel,
2020) based on a German-language publication by Brassel,
1974b). The elevation-dependent scale factor is ezln k,
where ln k is a user-defined parameter to control the
amount of contrast increase and decrease. The relative
elevation zis the result of a linear mapping of elevation
to the range [−1, +1]. The lowest point is mapped to −1,
the mean elevation is mapped to 0, and the highest eleva-
tion is mapped +1 (Brassel, 1974a).
g0¼ggf
 ezln kþgf(1)
Figure 2 shows a shaded relief image without Brassel’s
aerial perspective (left) and with aerial perspective (cen-
ter). Contrast of low areas is effectively reduced for
elevations below the mean elevation of the terrain
model. However, Brassel’s algorithm tends to increase
contrast for high elevations too strongly; high mountain
ridges in Figure 2 (center) are overly accentuated and
rendered as solid black areas.
Brassel’s original algorithm can be made more flex-
ible by adding a user-defined “pivot” elevation zp for
Figure 1. Shaded relief without aerial perspective (left) and with
aerial perspective (right) (Charleston Peak, Nevada, USA). Aerial
perspective is computed with our method. It reduces contrast of
low elevations, while maintaining strong contrast of high
elevations.
Figure 2. Shading without Brassel’s aerial perspective (left) and
with aerial perspective with ln k=1.3 (center). User-defined pivot
elevation zp to map elevation z to zbetween −1 and +1 (Glacier
National Park, Montana, USA).
2B. JENNY AND T. PATTERSON
specifying the elevation above which contrast is
increased and below which contrast is decreased
(Jenny, 2000). Figure 2 (right) illustrates the mapping
of elevation z (along the x-axis) to z(along the y-axis)
using two linear equations separated by the pivot eleva-
tion zp.
Serebryakova et al. (2015) applied Brassel’s aerial
perspective to individual watersheds. Because the
watershed boundaries were extracted from a digital ele-
vation model, Serebryakova et al. computed the zfor
individual watersheds instead of the entire elevation
model. This aimed at a more balanced application of
aerial perspective throughout a large elevation model.
An alternative algorithm for simulating aerial perspec-
tive in shaded relief was introduced by Jenny (2001) and
combines three components: (1) the elevation of a pixel
relative to the minimum and maximum elevation of the
terrain model; (2) the relative elevation of the pixel within
a slope, which is determined by tracing a slope line passing
through the pixel; and (3) the difference between the aspect
angle of the terrain at the pixel and the direction of illumi-
nation. This algorithm has two shortcomings. First, the
tracing of slope lines can get trapped in a local terrain
extremum when the slope line reaches a peak or a pit,
which results in a spotty distribution of the aerial perspec-
tive correction. Second, the orientation toward the illumi-
nation may not be available with some shading methods,
such as when blending multiple shadings with different
illumination direction or when computing shadings meth-
ods that do not explicitly model a direction of illumination,
such as shading with neural networks.
As shown by Patterson (2001a), raster graphics software
can add aerial perspective to shaded relief by combining
a contrast-reduced copy of a shaded relief with the original
shading using an elevation mask. Figure 3 illustrates this
technique. In Adobe Photoshop, an adjustment curve layer
is added to a shaded relief (Figure 3, left). The adjustment
curve is shaped to reduce contrast (Figure 3, right), but
does not make major changes to the tone for flat areas (the
vertical peak in the histogram). The effect of the curve on
the original shaded relief is controlled by a mask (high-
lighted in yellow in Figure 3, left); the mask’s pixel values
are modified elevation values. This method can be used to
increase and decrease contrast with elevation. It provides
flexibility and the option to make local adjustments if
needed. However, this method is rather cumbersome to
control. Our proposed method for adding aerial perspec-
tive described in the following section builds on this
method.
Neural network shading is a recently developed digital
method for replicating hand-drawn shaded relief (Jenny
et al., 2020). First, a deep neural network is trained with
manual shaded relief images, and then the network gener-
ates a shaded relief using a digital elevation model of
another area. The network learns essential design principles
such as locally adjusting the direction of illumination to
accentuate individual terrain features or varying brightness
and contrast to imitate the aerial perspective effect applied
in manual relief shading. Figure 4 shows that neural net-
work shading can imitate the aerial perspective effect to
some degree; high elevations are shown with stronger con-
trast than low elevations. The development of our method
for adjusting aerial perspective was inspired by the desire to
allow users of neural network relief shading to explicitly
and precisely control the amount of aerial perspective.
3. Aerial perspective
Our method for applying aerial perspective to a shaded
relief is inspired by the raster-based technique for reducing
Figure 3. Contrast reduction of low elevations with Adobe Photoshop curve layer (right) and mask consisting of modified elevation
values (left). Contrast is reduced in the lowlands (Churfirsten, Switzerland standard elevation model from Kennelly et al., 2020).
CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 3
luminance contrast suggested by Patterson (2001a). We
convert the raster graphics approach to two equations. The
first equation reduces the amount of contrast. It imitates
the effect of an adjustment curve and produces grayscale
values with aerial perspective applied everywhere.
The second equation imitates the effect of the elevation
mask and blends the values of the original shaded relief
with the values generated by the first equation.
Contrast of a grayscale pixel value v is reduced to v0
with Equation 2, ensuring that the gray value of flat areas
vf does not change (Figure 5, left). The user controls the
amount of contrast reduction with the parameter Δ,
which is the gray value assigned to a hypothetical black
pixel at the lowest elevation in the terrain model.
v0¼ΔþvfΔ
vfvwith v0;vfand v20;1½ 
and Δ vf(2)
The final gray value v00 is computed by linearly blend-
ing the original pixel value v with v0(Equation 3).
Equation 4 computes the weight w for Equation 3.
v00 ¼wv0þ1wð Þ  v(3)
w¼1z
zt
 k with w, z and zt20;1½ and k0 (4)
In Equation 4, z is the elevation of the pixel scaled to the
range 0;1½ with z¼zzmin
ð Þ=zmax zmin
ð Þ, and zmin
and zmax are the lowest and highest elevation in the model.
Parameter k controls the vertical distribution of the aerial
perspective effect. The elevation threshold parameter zt
limits the effect to low elevations. Figure 5 (right) illustrates
Equation 4. In this example, the elevation threshold zt was
set to 0.7, resulting in aerial perspective adjustment applied
to pixels below 70% of the maximum elevation.
The Δ parameter controls the amount of contrast
reduction. Because Δ is required to be smaller than the
gray value of flat areas (i.e. Δvf), a graphical user
interface can let the user select a parameter s between 0
and 1, which is mapped to Δ with Δ¼svf. This sim-
plifies Equation 2 to v0¼svfþ1sð Þ  v. Figure 6
illustrates the effect of increasing Δ. Contrast reduction
Figure 4. Standard Lambertian diffuse shading as commonly used for relief shading (left) compared to neural network shading (right).
The neural network shading shows high elevations with stronger contrast than low elevations (North Caucasus, Kabardino-Balkarian
Republic, Russia, SRTM elevation model, 85 × 85 km).
Figure 5. Left: Linear contrast reduction of pixel values v to
v0without changing the gray value of flat areas. Right:
Illustration of Equation 4 for computing blending weight w
from relative elevation z.
4B. JENNY AND T. PATTERSON
is strongest for low elevations and decreases towards high
elevations. A recommendable value for many elevation
models is Δ¼2
=
3vf.
The parameter k controls the vertical distribution of the
contrast reduction (Figure 7). The k parameter is less
important than Δ, because the visual effect of different
values for k is less intuitive to predict. Designers of software
applications may reduce the number of options offered in
a user interface, and select a constant value for k. If execu-
tion time is of concern, k can be set to 1 or 2, which
eliminates the call of the computationally expensive pow
function. Otherwise, recommendable values for k are
between 1 and 4.
We found that for most applications, zt is best set to
100%, that is, the contrast reduction applies throughout
the entire range of elevation values. With zt always equal
to 100%, the user interface can also be simplified.
The gray value of flat areas is preserved as with Brassel’s
method; its gray value vf is simple to compute for
Lambertian diffuse shading (Yoéli, 1965) by using
a vertical normal vector for flat areas. For shading methods
that do not explicitly model terrain normal vectors (for
example, when transferring shading with a neural net-
work), the value of flat areas can be determined by shading
a small patch of a flat elevation model. Hence, our method
does not require identifying flat areas in a digital elevation
model.
4. Example
Figure 8 compares our method for aerial perspective simu-
lation to Brassel’s 1974 method. We rendered the top image
with a neural network that was trained with shaded reliefs
created for the Swiss national map series (Jenny et al.,
2020), which produced only a weak aerial perspective effect.
This shading was then enhanced by adding aerial perspec-
tive with Brassel’s 1974 method (middle) and with our
method (bottom). The top shading without explicit aerial
perspective simulation follows established design principles
for relief shading: large landforms are accentuated, the
direction of illumination adjusts to important landforms,
and a consistent tone is applied to flat areas. However, the
aerial perspective effect is weak; the highest peaks are
shown with the same contrast in luminance as the lowland.
The aerial perspective added with Brassel’s method cre-
ates a very strong luminance contrast (Figure 8, middle).
While the contrast of lowland areas is appropriately
reduced (compare the left-most area on the top and the
middle figure), the shaded slopes of high elevations become
excessively dark. The darkest and also the brightest areas
show very little tonal variation, because Brassel’s aerial
perspective simulation can result in dark values that are
negative and need to be corrected to a zero value, or
extremely bright values that are greater than the maximum
representable value for white. This results in the loss of
subtle shading gradients and eliminates small details in the
highest areas. This loss of information due to “overshooting
white” and “undershooting black“ values is exacerbated by
the overall increase of image brightness that is necessary
Figure 6. Increasing amount of aerial perspective n from top left
to lower right; vf is the value of flat areas between 0 and 1; k¼
1:5 for all four shadings (Valdez, Alaska standard elevation
model from Kennelly et al., 2020).
Figure 7. Increasing k from top left to lower right; Δ¼vf for all
four shadings.
CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 5
before combining the shaded relief with other raster and
vector features for the final map.
The aerial perspective added with our method only
reduces the absolute difference between bright and dark
pixels and avoids losing information that may occur
with Brassel’s method (Figure 8, bottom). The overall
image is considerably brighter than with Brassel’s
method. The transition in contrast from lowlands to
the highest peaks is subtle, as suggested by Imhof
(1982, p. 173), but there is still a clear difference in
Figure 8. Shaded relief without aerial perspective (top), with aerial perspective applied using Brassel’s method (middle), and our
method (bottom) (Wasatch Range, Utah, USA, the elevation model and shaded relief images are available at Jenny & Patterson, 2020).
6B. JENNY AND T. PATTERSON
luminance contrast between the highest peaks and the
considerably lower foothills. Figure 9 shows the differ-
ence between the original shading without aerial per-
spective of Figure 8 (top) and the shaded relief with
aerial perspective computed with our method in
Figure 8 (bottom). White and bright gray in Figure 9
indicate areas that were not or only minimally changed
by aerial perspective. As expected, these areas include
flat planes on the left of Figures 8 and 9, where the tone
of flat plains did not change. The highest peaks also did
not change much, which confirms visually that contrast
reduction decreases gradually with increasing elevation.
Dark red indicates areas with the largest absolute differ-
ence between the two shadings. These areas are mainly
shaded slopes at low and intermediate elevations, where
contrast reduction is strong.
5. Conclusion
We introduce a relatively straightforward method that is
easy to add to existing relief rendering software for com-
puting shaded relief from digital elevation models. The
method can be applied after a grayscale value has been
computed from an elevation model using any digital
shading algorithm. It builds on a raster graphics method
for elevation-dependent adjustment of contrast, but our
method makes it easier for the user to control and adjust
the amount and the vertical distribution of the aerial
perspective effect. Our method also improves upon an
existing method by Brassel (1974a) by not excessively
darkening high elevations. In many cases, the resulting
lighter shaded relief is applicable to cartographic produc-
tion “as is” without the need for additional adjustments.
Acknowledgments
The authors thank the anonymous reviewers for their valu-
able comments and Brooke E. Marston for copy editing this
paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Bernhard Jenny http://orcid.org/0000-0001-6101-6100
Tom Patterson http://orcid.org/0000-0003-4813-895X
Data Availability Statement
Java source code for applying aerial perspective to a shaded
relief image is openly available in the Zenodo repository,
combined with sample data for creating Figure 8 (shaded
relief images without and with aerial perspective, and the
corresponding digital elevation model of the Wasatch
Range, Utah, USA) at Jenny and Patterson (2020) “Aerial
Perspective for Shaded Relief”, https://doi.org/10.5281/
zenodo.3947033.
References
Bernabé-Poveda, M. A., & Çöltekin, A. (2015). Prevalence of
the terrain reversal effect in satellite imagery. International
Journal of Digital Earth, 8(8), 640–655. https://doi.org/10.
1080/17538947.2014.942714
Biland, J., & Çöltekin, A. (2017). An empirical assessment of
the impact of the light direction on the relief inversion
effect in shaded relief maps: NNW is better than NW.
Figure 9. Absolute difference between shading without aerial perspective (Figure 8 top) and with aerial perspective (Figure 8 bottom).
White indicates areas that were not changed by aerial perspective; dark red indicates the largest absolute differences.
CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 7
Cartography and Geographic Information Science, 44(4),
358–372. https://doi.org/10.1080/15230406.2016.1185647
Brassel, K., (2020), Correction to Brassel. (1974). Cartography
and Geographic Information Science. Advance online publi-
cation. https://doi.org/org/10.1080/15230406.2020.1813993.
Brassel, K. (1974a). A model for automatic hill-shading. The
American Cartographer, 1(1), 15–27. https://doi.org/10.1559/
152304074784107818
Brassel, K. (1974b). Ein Modell zur automatischen
Schräglichtschattierung. In K. Kischbaum & K.-H. Meine
(Eds.), International yearbook of cartography (pp. 66–77).
Kirschbaum.
Çöltekin, A., & Biland, J. (2019). Comparing the terrain reversal
effect in satellite images and in shaded relief maps: An exam-
ination of the effects of color and texture on 3D shape percep-
tion from shading. International Journal of Digital Earth, 12
(4), 442–459. https://doi.org/10.1080/17538947.2018.1447030
Dosher, B. A., Sperling, G., & Wurst, S. A. (1986). Tradeoffs
between stereopsis and proximity luminance covariance as
determinants of perceived 3D structure. Vision Research, 26
(6), 973–990. https://doi.org/10.1016/0042-6989(86)90154-9
Dresp-Langley, B., & Reeves, A. (2014). Effects of saturation
and contrast polarity on the figure-ground organization of
color on gray. Frontiers in Psychology, 5, 1136. https://doi.
org/10.3389/fpsyg.2014.01136
Egusa, H. (1983). Effects of brightness, hue, and saturation on
perceived depth between adjacent regions in the visual field.
Perception, 12(2), 167–175. https://doi.org/10.1068/p120167
Gerardin, P., de Montalembert, M., & Mamassian, P. (2007).
Shape from shading: New perspectives from the Polo Mint
stimulus. Journal of Vision, 7(11), 13. https://doi.org/10.1167/
7.11.13
Guibal, C. R., & Dresp, B. (2004). Interaction of color and
geometric cues in depth perception: When does “red” mean
“near”? Psychological Research, 69(1–2), 30–40. https://doi.
org/10.1007/s00426-003-0167-0
Imhof, E. (1982). Cartographic relief presentation. De Gruyter.
Jenny, B. (2000). Computergestützte Schattierung in der
Kartografie/Estompage assisté par ordinateur en cartogra-
phie [Unpublished master’s thesis]. ETH Zürich. http://
dx.doi.org/10.5169/seals-235683
Jenny, B. (2001). An interactive approach to analytical relief
shading. Cartographica: The International Journal for
Geographic Information and Geovisualization, 38(1&2),
67–75. https://doi.org/10.3138/F722-0825-3142-HW05
Jenny, B., Heitzler, M., Singh, D., Farmakis-Serebryakova, M.,
Liu, J. C., & Hurni, L. (2020). Cartographic relief shading
with neural networks. IEEE Transactions on Visualization
and Computer Graphics.
Jenny, B., & Hurni, L. (2006). Swiss-style colour relief shading
modulated by elevation and by exposure to illumination.
The Cartographic Journal, 43(3), 198–207. https://doi.org/
10.1179/000870406X158164
Jenny, B., & Patterson, T. (2020) Aerial perspective for shaded
relief [Data set]. Zenodo. https://doi.org/10.5281/zenodo.
3947033
Kennelly, P., Patterson, T., Jenny, B., Huffman, D., Marston, B.,
Bell, S., & Tait, A. (2020). Elevation models for reproducible
evaluation of terrain representation. The Cartographic
Journal. Advanced online publication. https://doi.org/10.
1080/15230406.2020.1830856
Kennelly, P. J., & Stewart, A. J. (2006). A uniform sky
illumination model to enhance shading of terrain and
urban areas. Cartography and Geographic Information
Science, 33(1), 21–36. https://doi.org/10.1559/
152304006777323118
Kleffner, D. & Ramachandran, V. S. (1992). On the perception
of shape from shading. Perception & Psychophysics, 52(1),
18–36. https://doi.org/10.3758/BF03206757
Mark, R. (1992). Multidirectional, oblique-weighted, shaded-
relief image of the Island of Hawaii. No. 92-422. US Dept. of
the Interior, US Geological Survey.
Marston, B. E., & Jenny, B. (2015). Improving the representa-
tion of major landforms in analytical relief shading.
International Journal of Geographical Information Science,
29(7), 1144–1165. https://doi.org/10.1080/13658816.2015.
1009911
Patterson, T., (2001a). Creating Swiss-style shaded relief in
Photoshop. http://www.shadedrelief.com/shading/Swiss.
html
Patterson, T. (2001b). See the light: How to make illuminated
shaded relief in Photoshop 6.0. http://www.shadedrelief.
com/illumination/index.html
Schwartz, B. J., & Sperling, G. (1983). Luminance controls the
perceived 3-D structure of dynamic 2-D displays. Bulletin
of the Psychonomic Society, 21(6), 456–458. https://doi.org/
10.3758/BF03330007
Serebryakova, M., Veronesi, F., & Hurni, L. (2015). Sine wave,
clustering and watershed analysis to implement adaptive illu-
mination and generalisation in shaded relief representations.
In Proceedings of the 27th International Cartographic
Conference, paper 597.
Sun, J., & Perona, P. (1998). Where is the Sun? Nature
Neuroscience, 1(3), 183–184. https://doi.org/10.1038/
630
Tai, N. C., & Inanici, M. (2012). Luminance contrast as depth
cue: Investigation and design applications. Computer-aided
Design and Applications, 9(5), 691–705. https://doi.org/10.
3722/cadaps.2011.691-705
Tait, A. (2002). Photoshop 6 tutorial: How to create basic
colored shaded relief. Cartographic Perspectives, 42, 12–17.
https://doi.org/10.14714/CP42.550
Veronesi, F., & Hurni, L. (2014). Changing the light azimuth in
shaded relief representation by clustering aspect. The
Cartographic Journal, 51(4), 291–300. https://doi.org/10.
1179/1743277414Y.0000000100
Veronesi, F., & Hurni, L. (2015). A GIS tool to increase the
visual quality of relief shading by automatically changing
the light direction. Computers & Geosciences, 74, 121–127.
https://doi.org/10.1016/j.cageo.2014.10.015
Ware, C. (2019). Information visualization: Perception for
design. Morgan Kaufmann.
Yoëli, P. (1965). Analytical hill shading (a cartographic
experiment). Surveying and Mapping, 25(4), 573–579.
Yoëli, P. (1966). The mechanisation of analytical hill shading.
The Cartographic Journal, 4(2), 82–88. https://doi.org/10.
1179/caj.1967.4.2.82
Zhang, X., Chan, K. L., & Constable, M. (2014). Atmospheric
perspective effect enhancement of landscape photographs
through depth-aware contrast manipulation. IEEE
Transactions on Multimedia, 16(3), 653–667. https://doi.
org/10.1109/TMM.2014.2299511
8B. JENNY AND T. PATTERSON
... Light that has a wavelength that is shorter than or similar to these particles is scattered more. This phenomenon is called Rayleigh scattering in optics 2 , and atmospheric or aerial perspective in cartography and painting (Jenny and Patterson 2020). It is responsible for a decrease in contrast for distant objects and for giving them a bluish aspect, as blue light corresponds to the short wavelengths of the human visible spectrum. ...
... Many efforts are now available to make aerial perspectives with reliefs, such as luminance adjustments (Brassel 1974, Jenny andPatterson 2021), elevation masks (Patterson 2001), and neural networks (Jenny et al. 2020). ...
Preprint
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Terrain mapping is not only dedicated to communicating how high or how steep a landscape is but can also help to narrate how we feel about a place. However, crafting effective and expressive hypsometric tints is challenging for both nonexperts and experts. In this paper, we present a two-step image-to-terrain color transfer method that can transfer color from arbitrary images to diverse terrain models. First, we present a new image color organization method that organizes discrete, irregular image colors into a continuous, regular color grid that facilitates a series of color operations, such as local and global searching, categorical color selection and sequential color interpolation. Second, we quantify a series of subjective concerns about elevation color crafting, such as "the lower, the higher" principle, color conventions, and aerial perspectives. We also define color similarity between image and terrain visualization with aesthetic quality. We then mathematically formulate image-to-terrain color transfer as a dual-objective optimization problem and offer a heuristic searching method to solve the problem. Finally, we compare elevation tints from our method with a standard color scheme on four test terrains. The evaluations show that the hypsometric tints from the proposed method can work as effectively as the standard scheme and that our tints are more visually favorable. We also showcase that our method can transfer emotion from image to terrain visualization.
... Global data sets are particularly demanding here, as the global relief trends as well as important local relief nuances have to be balanced well. Here, connections to terrestrial developments would likely turn out to be very helpful (see, e.g., (Horn, 1981, Jenny and Patterson, 2021 geological map, a large number of additional elements are required on the map sheet, which both in their representation and in their positioning influence the understanding of the entire map sheet. This fact is the reason why the map sheet design is also assigned as an open issue. ...
Conference Paper
This contribution provides a concise review of the current developments and challenges in the domain of planetary cartography. Considered to be one of the more exotic branches of cartography, it currently re-positions itself due to (1) an increasing community-centric research interest, but also due to (2) the current development in the field of space exploration led by industry as well as ambitious international countries. Imaging, mapping and cartographic compilation have always been the primary tools for exploring terrain, and while the terrestrial planets have been mapped in some relative detail, planetary cartography is still largely stuck at medium map scales. While planetary cartography shares some similarities with developments in the field of terrestrial cartography, it developed largely differently and thus requires in-depth discussion about how these new challenges can be addressed and eventually solved. Advice and support from the terrestrial cartographic community is highly needed in order to develop sustainable long-term strategies.
... Global data sets are particularly demanding here, as the global relief trends as well as important local relief nuances have to be balanced well. Here, connections to terrestrial developments would likely turn out to be very helpful (see, e.g., (Horn, 1981, Jenny and Patterson, 2021). ...
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This contribution provides a concise review of the current developments and challenges in the domain of planetary cartography. Considered to be one of the more exotic branches of cartography, it currently re-positions itself due to (1) an increasing community-centric research interest, but also due to (2) the current development in the field of space exploration led by industry as well as ambitious international countries. Imaging, mapping and cartographic compilation have always been the primary tools for exploring terrain, and while the terrestrial planets have been mapped in some relative detail, planetary cartography is still largely stuck at medium map scales. While planetary cartography shares some similarities with developments in the field of terrestrial cartography, it developed largely differently and thus requires in-depth discussion about how these new challenges can be addressed and eventually solved. Advice and support from the terrestrial cartographic community is highly needed in order to develop sustainable long-term strategies.
... However, reading contour lines (Samsonov et al., 2019) is difficult for many map users and requires considerable training. We are exploring more intuitive and immediate illustrative methods for communicating the threedimensional shape of Earth's surface (Jenny et al., 2020b;Jenny, 2020), including lightweight interactive camera control techniques for 3D terrain maps on interactive surfaces (Danyluk et al., 2019) or cartographic relief shading (Jenny and Patterson, 2020). We are inspired by masters of the manual cartographic art, and replicate Swiss-style relief shading using neural networks that we train with manually drawn shaded relief masterpieces (Jenny et al., 2020a) (Fig. 9). ...
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This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia (Monash IA Lab). The lab has been influential with contributions in algorithms, interaction techniques and experimental results in Network Visualisation, Interactive Optimisation and Geographic and Cartographic visualisation. It has also been a leader in the emerging topic of Immersive Analytics, which explores natural interactions and immersive display technologies in support of data analytics. We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.
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Alpine topography is formed by a complex series of geomorphological processes that result in a vast number of different landforms. The youngest and most diverse landforms are various Quaternary sedimentary bodies, each characterised by its unique landform features. The formation of Quaternary sedimentary bodies and their features derive from the dominant building sedimentary processes. In recent years, studies of Quaternary sedimentary bodies and processes have been greatly aided by the use of digital elevation models (DEMs) derived by airborne laser scanning (ALS). High-resolution DEMs allow detailed mapping of sedimentary bodies, detection of surface changes, and recognition of the building sedimentary processes. DEMs are often displayed as hillshaded reliefs, the most common visualisation technique, which suffers from the limitation of a single illumination source. As a result, features can be barely visible or even invisible to the viewer if they are parallel to the light source or hidden in the shadow. These limitations become challenging when representing landforms and subtle landscape features in a diverse alpine topography. In this study, we focus on eleven visualisations of Quaternary sedimentary bodies and their sedimentary and morphological features on a 0.5 m resolution DEM. We qualitatively compare analytical hillshading with a set of visualisation techniques contained in the Raster Visualisation Toolbox software, primarily hillshading from multiple directions RGB, 8-bit sky view factor and 8-bit slope. The aim is to determine which visualisation technique is best suited for visual recognition of sedimentary bodies and sedimentation processes in complex alpine landscapes. Detailed visual examination of previously documented Pleistocene moraine and lacustrine deposits, Holocene alluvial fans, scree deposits, debris flow and fluvial deposits on the created visualisations revealed several small-scale morphological and sedimentary features that were previously difficult or impossible to detect on analytical hillshading and aerial photographs. Hillshading from multiple directions resulted in a visualisation that could be universally applied across the mountainous and hilly terrains. In contrast, 8-bit sky view factor and 8-bit slope visualisations created better visibility and facilitated interpretation of subtle and small-scale (less than ten metres) sedimentary and morphological features.
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Shaded relief is an effective method for visualising terrain on topographic maps, especially when the direction of illumination is adapted locally to emphasise individual terrain features. However, digital shading algorithms are unable to fully match the expressiveness of hand-crafted masterpieces, which are created through a laborious process by highly specialised cartographers. We replicate hand-drawn relief shading using U-Net neural networks. The deep neural networks are trained with manual shaded relief images of the Swiss topographic map series and terrain models of the same area. The networks generate shaded relief that closely resemble hand-drawn shaded relief art. The networks learn essential design principles from manual relief shading such as removing unnecessary terrain details, locally adjusting the illumination direction to accentuate individual terrain features, and varying brightness to emphasise larger landforms. Neural network shadings are generated from digital elevation models in a few seconds, and a study with 18 relief shading experts found that they are of high quality.
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Relief shading is the most common type of cartographic relief representation for print and digital maps. Manual relief shading results in informative and visually pleasing representations of terrain, but it is time consuming and expensive to produce. Current analytical relief shading can be created quickly, but the resulting maps are not as aesthetically appealing and do not show landscape features in an explicit manner. This article introduces an automated digital method that produces shaded relief with locally adjusted illumination directions to simulate the techniques and cartographic principles of manual relief shading. Ridgelines and valley lines are derived from a digital terrain model, vectorized, and used in a diffusion curve algorithm. A graph analysis generalizes the lines before using them for diffusion curve shading. The direction of illumination is adjusted based on the spatial orientation of ridgelines and valley lines. The diffusion curve shading is combined with standard analytical relief shading to create a final diffusion relief shading image. Similar to manual relief shading, major landforms and the structure of the terrain are more clearly shown in the diffusion relief shading. The presented method best highlights major landforms in terrain characterized by sharp, clearly defined ridges and valleys.
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