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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,
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Aerial perspective for shaded relief
Bernhard Jenny
and Tom Patterson
Faculty of Information Technology, Monash University, Melbourne, Australia;
U.S. National Park Service (Retired), Harpers Ferry, WV, USA
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.
Received 26 May 2020
Accepted 18 August 2020
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
© 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 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).
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
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).
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
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).
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.
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)
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.
is strongest for low elevations and decreases towards high
elevations. A recommendable value for many elevation
models is Δ¼2
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
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.
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).
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.
The authors thank the anonymous reviewers for their valu-
able comments and Brooke E. Marston for copy editing this
Disclosure statement
No potential conflict of interest was reported by the authors.
Bernhard Jenny
Tom Patterson
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”,
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.
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, 44(4),
Brassel, K., (2020), Correction to Brassel. (1974). Cartography
and Geographic Information Science. Advance online publi-
Brassel, K. (1974a). A model for automatic hill-shading. The
American Cartographer, 1(1), 15–27.
Brassel, K. (1974b). Ein Modell zur automatischen
Schräglichtschattierung. In K. Kischbaum & K.-H. Meine
(Eds.), International yearbook of cartography (pp. 66–77).
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.
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.
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.
Egusa, H. (1983). Effects of brightness, hue, and saturation on
perceived depth between adjacent regions in the visual field.
Perception, 12(2), 167–175.
Gerardin, P., de Montalembert, M., & Mamassian, P. (2007).
Shape from shading: New perspectives from the Polo Mint
stimulus. Journal of Vision, 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.
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://
Jenny, B. (2001). An interactive approach to analytical relief
shading. Cartographica: The International Journal for
Geographic Information and Geovisualization, 38(1&2),
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.
Jenny, B., & Patterson, T. (2020) Aerial perspective for shaded
relief [Data set]. Zenodo.
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.
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.
Kleffner, D. & Ramachandran, V. S. (1992). On the perception
of shape from shading. Perception & Psychophysics, 52(1),
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.
Patterson, T., (2001a). Creating Swiss-style shaded relief in
Patterson, T. (2001b). See the light: How to make illuminated
shaded relief in Photoshop 6.0. http://www.shadedrelief.
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.
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.
Tai, N. C., & Inanici, M. (2012). Luminance contrast as depth
cue: Investigation and design applications. Computer-aided
Design and Applications, 9(5), 691–705.
Tait, A. (2002). Photoshop 6 tutorial: How to create basic
colored shaded relief. Cartographic Perspectives, 42, 12–17.
Veronesi, F., & Hurni, L. (2014). Changing the light azimuth in
shaded relief representation by clustering aspect. The
Cartographic Journal, 51(4), 291–300.
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.
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.
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.
... 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 1 , and atmospheric or aerial perspective in cartography and painting [JP20b]. 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. ...
... Aspect-based Lambertian with LLA Aspect-based with LLA We also apply aerial perspective following the model by [JP20b]. ...
... Here, we compare LLA-enhanced Lambertian results with the recent Relief Shading with Neural Networks technique by Jenny et al. [Jen+21]. The neural-based shading also displays aerial perspective using the Aerial Perspective Shaded Relief method [JP20b]. We implemented it as well for the sake of a fair comparison. ...
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A realistic depiction of a scene is often not the optimal choice to efficiently convey a message. In this thesis, we investigate how visual artists control lighting to influence our perception of physical properties of the scene. We are particularly interested in their uses of shading and cast shadows to depict shape and depth information. We find a particular case-study in the style of the hand-painted panorama maps of Pierre Novat (1928-2007), who excelled at depicting complex mountainous landscapes. We study Novat’s pictorial style and how he freed himself from depicting mountains realistically, in favour of effectively transmitting the necessary information for terrain understanding.Drawing on Vision Science and our study of Novat’s artworks, we introduce novel methods aimed at enhancing the depiction of shape and depth information in 3D renderings:Our first method, Local Light Alignment, focuses on enhancing shape depiction at multiple scales by controlling the shading intensity locally at the surface. We change the light direction at each surface point to ensure congruence between the actual physical shape and its shading patterns. We extend our approach to control material components independently, e.g., highlights and refractions.Our second approach focuses on cast shadows. They can at the same time mask areas, hindering our perception, as well as provide our visual system with depth, shape, and spatial arrangement information. Our method computes geometry-dependent light directions ensuring a correct placement of cast shadows. We also propose multi-scale cast shadows to reintroduce lost depth and shape cues in already shadowed areas. Finally, we show the effectiveness of our lighting editing algorithms in the context of analytical shading (2D maps), as well as for 3D panorama maps.
... Brightness values of optical images represent the reflected power of solar radiation. To simulate an optical image, the Lambert shading algorithm is employed to approximate the relationship of reflected energy with the local incidence angle (γ i ) [17]. In this algorithm, it is assumed that the surface of simulated image has Lambertian behavior, with the same amount of reflectance in all view directions. ...
Registration of multimodal images such as optical data and digital elevation models (DEM) is a challenging task due to significant non-linear differences between these images. To address the problem, this paper proposes a new robust approach to integrate optical images and DEMs by using terrain features. In order to detect these features, a simulated image is generated based on the DEM by illuminating it in the geometry of the optical image acquisition, such that typical textures induced by topography are well present as those in the optical image. Hence, the multimodal registration is performed on the optical data and the simulated image to maximize the accuracy of feature matching. The Affine Scale-Invariant Feature Transform (ASIFT) is used to detect keypoints from the two images. Correspondences are matched through the nearest neighbor distance ratio, and outliers are removed using a two-step elimination procedure. The proposed method has been tested on five pairs of optical-DEM images with various spatial resolutions. Experimental results have shown that this method can provide robust registration for optical-DEM images with high accuracies of sub pixel level. The novelty of proposed method is to make use of terrain features for registration, providing a new perspective for the integration of multimodal images.
... 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. ...
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I present a study of the hand-painted winter panoramas of Atelier Novat, a workshop founded by Pierre Novat (1928–2007) in the 1960s, whose style was perpetuated by his children Arthur and Frédérique. I offer a portrait of Pierre Novat and a brief historical overview of the workshop. The contribution of the paper is to describe the style of Novat through the analysis of its constituent elements: creation process, color palette, terrain deformation, light effects, and surface texture (trees, rocks, roads, and buildings). Creating an ideal yet personal representation of a mountain has a dual purpose: a practical one, to help the viewer understand the topography of the region, and an aesthetic one, to depict an imaginary mountain, now iconic of the French Alps, that encourages dreams. The paper concludes with a review of existing methods, in cartography and computer graphics, for the creation of digital panoramas.
... 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). ...
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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.
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This doctorate dissertation, entitled: "Multi-directional Analytical Hill-Shading for Enhanced Cartographic Relief Presentation" studies the method of Multi-directional Hill-Shading (MDHS) and examines its potential to deal with the weakness of usual, one source hill-shading illustrating the entire shape of the earth's surface. This weakness is manifested by the complete hiding of the ground’s shape, when its orientation diverges from the light source, and by the incomplete rendering of its variations for the rest of the cases, where the surface is oriented parallel or perpendicular to the illumination. The framework of actions proposed to heal the above issues and enhance the visual effect of hill-shading is based on three axes: • Implementation of dominant hill-shading with optimal illumination • Implementation of a MDHS model • Selective enhancement of the dominant, optimal hill-shading with MDHS The ultimate goal of the thesis is to produce a hill-shading image that will maintain its intimate, realistic character and achieve maximum performance in the rendering of the information of relief. This combination is possible if a dominant illumination with strong tonal contrast, properly chosen to depict most of the terrain formations is maintained and enhanced in general and especially on the darkly shaded slopes with controlled participation of MDHS, in order to restore the incomplete information of the relief. The two initial chapters of the thesis contain the general background for understanding the problem. Chapter 1 provides a brief description of the peculiarities and simplifications of hill-shading procedure for cartographic use. The analytical equations and concepts involved in the computation of gray tones and a review of current developments and related studies in analytical hill-shading complete the theoretical background. In Chapter 2, after clarifying the role of illumination direction, proposals are given to improve the shortcomings in the use of only a single source, with gradual enrichment of illumination from local light source adaptation, then to combinations of different light directions and up to multi-source sky models, to finally describe the term Multi-Directional Hill-Shading (MDHS). This is followed, in Chapter 3, by the description of experimental evaluation of the visual complexity of single and multiple source hill-shading images using eye movement tracking. The analysis of observations was performed by calculating basic and derivative metrics of recorded observations and by integrated co-evaluation based on expert judgement. Finally, visual coherence of MDHS images with the familiar character of hill-shading images of topographic relief is established, despite the observed peculiarity of tonal equalization and the consequent lagging of tonal contrast, caused by multiple illumination. Chapters 4 and 5 frame with the necessary literature review and put into practice the three axes of the proposed methodology. Chapter 4 lists the existing models for MDHS synthesis and explores additional, alternative models, which are distinguished in terms of how the components hill-shading images are weighted into: global-weighted, oblique-weighted and incident-weighted (i.e. weighted based on the incidence angle of their illumination). Chapter 5 documents the perceptually correct, general NW illumination origin, and discusses integrated analytical methods of terrain analysis that can assist to determine a specific light direction (e.g., diagrammatic analysis of orientation data, structural line identification, land surface generalization, morphometric classification and analysis, etc.). In the final mixing between dominant, optimal single-source hill-shading and MDHS, both fixed and variable weighting is applied, by controlling the degree of correction with the local incidence angle of optimal illumination on the ground. Within the framework of the dissertation, practical application of MDHS models, as well as their combinations with single-source hill-shading was carried out in a mountainous study area of Kalavryta, by implementation in the integrated GIS software environment of ESRI's ArcGIS®. The visual results of selected combinations are evaluated in chapter 6, in terms of completeness in the rendering of topographic relief, with an empirical survey implemented as an online questionnaire and filled by scientific personnel with experience in cartographic and spatial visualizations. This is followed by statistical testing of the responses, to identify choices that achieve significantly improved performance, with a clear evidence that MDHS use is beneficial. In the conclusion of the thesis, the main objectives and the methodology for fulfilling them are summarized, and some open questions for further research are set out.
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Terrain maps are often composed of shaded relief along with other raster layers which we call thematic terrain layers to create aesthetically pleasing and clear maps of physical geography. Despite that the interplay of layers is of primary concern to a cartographer, much of the research on terrain mapping has focused on studying terrain layers individually. This research aimed to fill this gap by evaluating the effect of combining shaded relief with thematic terrain layers and assessing ratings of beauty, realism, and landform clarity in an exploratory online user study. Specifically, we tested the combination of: manual, multidirectional, and ray-traced shaded relief with three thematic terrain layers: hypsometric tinting, land cover, and orthoimagery. There are five main findings from this exploratory study: (1) there was a direct correlation between beauty and realism scores, (2) the manual relief we tested was consistently rated lowest for beauty, realism, and landform clarity, and orthoimagery was rated the highest for beauty and realism, (3) shaded relief was more influential than thematic terrain layers on landform clarity ratings, (4) participant’s geographic familiarity had a significant impact in four specific instances of the user study, and (5) neither shaded relief or thematic terrain layers were the sole contributors to map reader perceptions of beauty, realism, or landform clarity. We conclude by identifying limitations in our stimuli design and presenting ideas for future research studies on terrain design.
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Sjenčanje reljefa u švicarskom stilu učinkovita je i vizualno atrak-tivna metoda za predstavljanje treće dimenzije reljefa na dvodimenzionalnoj karti. Takva metoda korištena je za predstavljanje terena na prirodan, istančan i intuitivan način. Važan element kod takvog načina sjenčanja reljefa je upotreba prilagođene ljestvice boja i efekta atmosferske perspektive. U prvom dijelu ovoga rada opisan je grafički i tehnički razvoj, te osnovna načela sjenčanja reljefa u švicarskom stilu. Drugi dio rada prikazuje proces izrade sjenčanog reljefa iz-vedenog iz digitalnog modela reljefa korištenjem isključivo funkcionalnosti dostupnih unutar slobodnog geoinformacijskog sustava otvorenog koda QGIS. Nadahnuti djelovanjem švicarskog kartografa Eduarda Imhofa na području vi-zualizacije reljefa, izrađena je digitalna karta Švicarske primjenjujući tehnike klasične švicarske škole. Proces izrade karte sastoji se od nekoliko koraka pri čemu najvažniji dio čine izrada pojedinih slojeva karte dobivenih iz digitalnog modela reljefa (jednosmjerno sjenčanje, višesmjerno sjenčanje, zračna perspekti-va), izrada hipsometrijske ljestvice boja po uzoru na karte švicarskih kartografa, pronalazak optimalnih vrijednosti parametara pojedinih slojeva karte, te završna grafička obrada karte.
A terrain data model using orientation rather than elevation permits more efficient analysis and stores its data in a multi-band raster. Representation techniques from the computer graphics industry are readily adopted with this data model. A common data model for terrain surfaces–the raster digital elevation model (DEM)–carries with it limitations by emphasizing height. Derived products such as relief shading require additional processing to determine orientation, even though they are used more frequently than those relying on elevation (e.g. hypsometric tinting). We show some of the benefits of encoding and analyzing terrain based on surface orientation, an approach that uses normal vectors stored as multi-band raster, the data storage convention in the computer graphics industry. A change in the data model and the conceptualization of the surface’s defining characteristics allows relief shading methods to run faster than conventional tools. Processing efficiencies are especially useful for more advanced shading models that may employ hundreds of relief shading calculations. In addition, an orientation-focused approach to terrain permits cartographic techniques to parallel common computer graphics methods. This project explores one such method, normal-mapping, an effect that adds texture to conventional relief shading by perturbing surface normal vectors.
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This paper proposes elevation models to promote, evaluate, and compare various terrain repre- sentation techniques. Our goal is to increase the reproducibility of terrain rendering algorithms and techniques across different scales and landscapes. We introduce elevation models of varying terrain types, available to the user at no cost, with minimal common data imperfections such as missing data values, resampling artifacts, and seams. Three multiscale elevation models are available, each consisting of a set of elevation grids, centered on the same geographic location, with increasing cell sizes and spatial extents. We also propose a collection of single-scale elevation models of archetypal landforms including folded ridges, a braided riverbed, active and stabilized sand dunes, and a volcanic caldera. An inventory of 78 publications with a total of 155 renderings illustrating terrain visualization techniques guided the selection of landform types in the elevation models. The benefits of using the proposed elevation models include straightforward comparison of terrain representation methods across different publications and better documentation of the source data, which increases the reproducibility of terrain representations.
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Relief inversion (or terrain reversal) effect is a well-known phenomenon in cartography that occurs when shadow is the main depth cue for three-dimensional shape perception. Light direction has been suggested as the main cause of this effect. However, the prevalence of relief inversion effect with regard to the changing light direction is currently not established, and there is little empirical evidence on this subject. This article systematically assesses the influence of light direction on the accuracy of landform perception in shaded relief maps (SRM). In a controlled experiment, 27 participants were asked to identify concave and convex landforms in 128 SRMs using a 5-point Likert scale where answers varied from clearly a valley to clearly a ridge. Eight different scenes were illuminated from 16 light directions to obtain the 128 SRMs. Our findings clearly demonstrate that incident light at 337.5° north-northwest (NNW) yields the highest accuracy and confidence ratings in landform identification among the investigated light directions; and leads to higher accuracy scores than at the 315° (NW) which is conventionally used in SRMs. Thus, we propose an update to this convention and recommend the light source to be placed at 337.5° when creating SRMs.
Conference Paper
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Searching for a standardised way to replicate the Swiss style of relief shading, we developed several methods, such as sine wave and others including clustering and watershed analysis, and applied them to the shaded relief representation. The main technique is built on a sine wave equation and was developed to automatically change the light direction based on aspect. To allow terrain generalisation, we implemented two additional methods deploying clustering aspect and watershed analysis. Clustering is fully automated, though it requires some tuning to eliminate noise and to smooth the clustered areas. On the contrary, watershed analysis is not automated and depends on the user experience, but allows us to extract areas between ridges and drainage precisely. Finally, changes of tone implemented in the aerial perspective toolbox help to increase the contrast differences between valleys and ridges and as a consequence to highlight all the most important geomorphological shapes. These tools increase visual quality of shaded relief, standardise the process of producing hillshading and enable consistency of results.
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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.
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.
Terrain reversal effect (TRE) causes reversed 3D shape perception in satellite images and shaded relief maps (SRMs), and introduces difficulties in identifying landforms such as valleys and ridges. With this paper, in a controlled laboratory experiment, we compare how well 27 participants could identify valleys and ridges over 33 locations using SRMs, color satellite images and grayscale satellite images. The main depth cue is shadow both in vertical-view images and SRMs. However, the presence of texture and color in images also affect 3D shape perception. All our participants experience the illusion strongly: with the SRMs, it is very severe (2% accuracy), with grayscale images low but considerably better than SRMs (17.6% accuracy), and slightly worse with color imagery (15.3% accuracy). These differences between SRMs and imagery suggest that the participants who are able to bypass the illusion consciously or subconsciously interpret the photographic information. We support this observation further with a cue-strength analysis. Furthermore, we provide exploratory analyses of the effects of expertise, global convexity bias, and bistable perception. Our original empirical observations serve towards a better understanding of this visual illusion, and contribute towards nuanced and appropriate solutions to correcting for TRE differently for satellite images and SRMs.
The first part of this paper is the author's translation of his "Die Mechanisierung' der Analytischen Schattierung", which was first published in Kartographische Nachrichten, 16,3, 1966 and is included here by permission of the editors and publishers. A re-statement of the basic principles of the method is followed by an examination of the three main operations: the provision of data required for the computing of light intensity; the computation of the light intensity; and the graphic representation.In the second part the possibilities of the practical application of the method are treated in relation to the combination of shading and contours, hill-shading and air photographs, hill-shading and measurement of slopes, and hill-shading and reproduction.