Sarradin, F.

Siret, D.

Teller, Jacques

International audienceSeveral techniques have already been proposed to study the visual perception of the urban environment along a route or a path. Some of them are based on Gibson's concept of motion perspective. Motion perspective refers to the gradual change in the rate of displacement of contour lines in the visual field. Initially developed in the field of aviation psychology, this effect proved to be of paramount importance in the perception of any observer in displacement. In this paper, we propose to develop a computer method to analyze this motion perspective. Our method is based on Teller's spherical metric. It takes advantage of skeletonization, a technique heavily used in image analysis

Sarradin, François

Siret, Daniel

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Visual urban space assessment from sky shape analysis
François Sarradin, Daniel Siret, Jacques Teller
To cite this version:
François Sarradin, Daniel Siret, Jacques Teller. Visual urban space assessment from sky shape analysis.
CVPRIP 2003, Sep 2003, Cary, North Carolina, United States. pp. 696-699. ￿halshs-02472366￿
Visual urban space assessment from sky shape 
analysis 
François Sarradin1, Daniel Siret1, Jacques Teller2
1CERMA UMR CNRS 1563 
School of Architecture of Nantes 
rue Massenet – BP 81931 
44319 NANTES CEDEX 3, France 
2LEMA 
University of Liège 
1, chemin des Chevreuils 
B-4000, LIÈGE1, Belgium 
1 Introduction 
Urban ambient environments are defined as the 
singular combination of factors that confers its 
perceptive distinctiveness to a space. These factors 
typically include noise and climate, amount of light 
or visual enclosure [13]. Different computer-based 
methods have recently been proposed to characterize 
the shape of urban open spaces. Field-oriented 
approaches have been defined by Teller as those 
computer methods which tend to assimilate ambient 
environments to a continuous field, whose 
characteristics vary throughout the open space. It is 
hereby proposed to adopt this ontology, in the view 
of characterizing the perception of observers in 
motion. 
It is actually well known that movement and 
speed deeply influence our perception of the 
environment. Several techniques have already been 
proposed to study the visual perception of the urban 
environment along a route or a path [7, 1, 21, 5]. 
Some of them are based on Gibson's concept of 
motion perspective [10]. Motion perspective refers to 
the gradual change in the rate of displacement of 
contour lines in the visual field. Initially developed in 
the field of aviation psychology, this effect proved to 
be of paramount importance in the perception of any 
observer in displacement. In this paper, we propose to 
develop a computer method to analyze this motion 
perspective. Our method is based on Teller's spherical 
metric [20]. It takes advantage of skeletonization—a 
technique heavily used in image analysis. 
Section 2 briefly reviews existing computer-
methods to analyze the visual perception of urban 
environments. We then propose in section 3 a 
description of our method, based on a combination of 
Teller's spherical projections and the skeletonization 
technique in order to analyze the motion perspective. 
Some measures, derived from our method, are 
described in section 4. An interpretation of 
preliminary results is then proposed. It is especially 
discussed how these measures can help to determine 
sequences in the observer route. Section 5 is a brief 
conclusion about the presented measures and their 
applications. 
2 Background 
Different field-oriented methods have been proposed 
to relate the visual attraction of an urban 
environment, the way we see it, and its morphology 
[17]. Following Gibson's perception theory [10], 
Benedikt developed isovists which compute the shape 
on a plane of our visual field and study its 
geometrical surface [2]. Space syntax theory, 
developed by Hillier and Hanson proposes graph 
analysis of visibility relationships within buildings 
and urban configurations [11]. Turner's visibility 
graphs build upon this kind of analysis combined 
with isovists [22]. It links the potential locations of an 
observer between them if they are visible one from 
the other. Peponis' e-partitions divide the space into 
visually homogeneous regions, which enable to study  
the visual events in urban spaces [15]. 
Teller's spherical metric proposes to consider the 
shape of the sky perceived from given locations 
within the open space [20]. The variation of some 
parameters of this sky shape, as for instance its 
opening, is used to highlight the underlying structure 
of urban open spaces—limits, central area, main axes. 
The main advantage of this technique, when 
compared to the previous ones, is to consider the 
third dimension of urban open spaces while allowing 
to cope with its non-bounded character (the sky is 
assumed to be at an infinite distance from the 
ground). Several indicators of urban open spaces can 
be defined, which estimate the opening of sky shape, 
its eccentricity or its spreading. 
All of these methods are based on a field-oriented 
approach of the urban open space. They are intended 
to highlight variations within the open space, which 
is no longer considered as static. They hence 
constituted an important step towards a better 
consideration of the dynamics of perception. Still 
none of these methods have been designed to 
characterize the dynamic variation of the visual 
environment, its progressive expansion, as it is 
perceived by an observer in motion. This effect, 
defined as the motion perspective by Gibson, would 
typically require to trace the deformation of visible 
shapes and apparition of new ones during movement. 
In this paper, we present a new method which 
consists in following these dynamic variations. 
3 Analysis of sky shape 
The method we propose is based on spherical 
analysis of open spaces. In spherical views, all the 
urban environment visible from a given point can be 
represented within a single view and the sky is 
central to the figure. Projections are computed from 
urban 3D models and produce discrete images whose 
properties can be analyzed. 
Our method consists in applying a morphological 
transformation to spherical projections. It extracts the 
shape of the sky from the picture and generates a 
simplified representation of this shape, defined as the 
skeleton or medial axis transform (MAT). According 
to the Blum’s definition of the MAT [3, 6], let S ⊂ R2 
be a shape, 
MAT(S) = {(p, r) ∈ R2 × [0, ∞) | Br(p): maximal 
 inscribed circle in S}, (1) 
where p is a skeletal point and r the radius it is 
associated with. More explicitly, MAT(S) is the set 
of all pairs of the centers of the maximal inscribed 
circles in S and their radius. MAT keeps the 
geometrical and topological information of the 
original shapes. It means that the shape can be 
reconstructed from information contained in the 
skeleton. It is heavily used to decompose shapes in 
image analysis, but it has not yet been used to 
analyze the urban environment. 
Figure 1 represents the first three steps of our 
method: the route selection in a virtual urban 
environment, spherical projections from successive 
points of the route, and the skeletonization of the 
projection centered on each point. The next step of 
our method consists in analyzing the deformation of 
those skeletons from one view to another one. 
Figure 1: Computation of urban skeletons. 
Several skeletonization algorithms have already 
been proposed in the literature [12, 9]. We decided to 
use the skeletonization method developed by Pavel 
and Siddiqi, which is called Hamilton-Jacobi 
skeletonization [8, 18]. According to the Teller’s 
spherical projection and the Blum's definition 
(equation 1), it provides the most accurate skeletons 
and the less sensitive one to the noise of the 
boundary. 
The sky shape boundaries are actually 
characterized by some noise, essentially due to the 
discretization process and lack of consistency of most 
urban 3D models. Effects of noise upon the results 
have to be strictly controlled even if spherical views 
tend to naturally reduce it (they give a greater 
importance to what is near the observer, which 
appears in detail, and compress what is far from the 
observer). 
4 Measures and interpretation 
Several measures can be performed with skeletons 
along a route in an urban environment. Here, we 
describe the greatest maximal disk (GMD) measure. 
The GMD is the skeletal point with the higher 
minimal distance from the boundary. Let S ⊂ R2 be a 
shape, GMD(S) is a pair of skeletal point and radius 
(p, r) ∈ MAT(S) such as ∀ (p’, r’) ∈ MAT(S), r ≥ r’. 
As it is not located near the end points of MAT, 
GMD is weakly influenced by the noise of the 
boundary. 
We will consider here two measures from the 
GMD: the GMD radius and the rear-front GMD 
location. In order to study it, we will test them along 
a route in a model of rue Xavier-Neujean in Liège, 
represented in figure 2. This route has four interesting 
sections: S1 is a church, S2 is a place, S3 is an open 
parking lot, and S4 is crossroads behind a theater. A 
projection of the sky is calculated every 5 meters on a 
route of 245 meters. 
Figure 2: The route in rue Xavier-Neujean in Liège. 
We now describe these two measures (GMD 
radius and GMD location) and present a way to 
combine them in order to characterize visual 
sequences of an urban route. 
S1
S2
S3 
S4 
4.1 Greatest maximal disk radius 
The radius of the GMD provides results comparable 
to Teller's open space indicators [20]. This kind of 
indicator estimates the sense of openness that an 
observer may feel in an urban space. Figure 3 
represents the variation of the GMD radius measure 
along the route in figure 2. The x-axis represents the 
image number (position in the path) while the y-axis 
gives the percentage of GMD radius divided by the 
radius of the projection circle (maximal value of 
GMD). 
It can be seen from the graph that the variation of 
GMD radius along the route is characterized by four 
peaks: position 11, around position 28, position 37, 
and at the end of the route. Except for the first peak, 
the others correspond to the sudden appearance of 
S2, S3, and S4 openings. The first peak in the graph 
corresponds to the visual opening characterizing 
points located between the start of the route and the 
bell tower of the church (S1). Minimums of the curve 
are also important. The first one is a transition 
between the space before the church and the place 
(S2). The next one is due to a narrow part between 
the place and the parking lot (S3). The last one is a 
long narrow crossing between S3 and S4. 
20
25
30
35
40
45
50
1 6 11 16 21 26 31 36 41 46
Figure 3: GMD radius variation along the route in 
figure 2. 
The GMD radius measure is not tied with the 
direction of the observer movement. But it detects 
local openings in the visual environment of the 
observer. Its variations highlight spaces with a high 
openness and transitions between these spaces. 
4.2 Relative GMD location 
Along a route, the location of the GMD varies around 
the center of the spherical projection: the GMD can 
be located in the front or in the back of the observer, 
with respect to his direction. By this way, it is 
possible to determine at which time an observer 
perceives the most important local opening. 
Figure 4 shows the rear-front location measure of 
the GMD, in the environment represented in figure 2. 
It is computed according to the center of the 
projection and the observer orientation in the route. 
When the value is superior to 0, the observer sees the 
GMD, otherwise it is in his back. 
-60
-40
-20
0
20
40
60
1 6 11 16 21 26 31 36 41 46
 
Figure 4: GMD rear-front location graphics for the 
route in figure 2. 
From the graph of figure 4, we can notice the four 
series of peaks. These are due to the appearance of 
the four openings described in the preceding section. 
For example, between positions 19 and 26, the 
observer can see an opening on his front before his 
entrance in place (S2). The entrance starts at the 
image 22. After image 26, the observer is still in the 
place but his visual field contains the narrow part 
between the place and the parking lot. This narrow 
part is a transition. 
The rear-front GMD location provides a mean to 
situate the local opening from a given location. In the 
route context, it highlights interesting visual events 
which are the appearance and disappearance of 
openings in the stream of urban shape variation. 
Hence, this measure is relative to the observer 
direction and it benefits of the same stability than the 
GMD radius measure. Moreover, it highlights 
openings in front of the observer. 
4.3 Combination between the GMD radius and 
the GMD location 
The GMD location describes a partition of the route 
into parts where GMD is in front of the observer and 
transition parts. The GMD radius provides 
information about the importance of the GMD. The 
combination of these two measures brings a rich 
interpretation of the selected route. 
Figure 5 represents the route partition according 
to the graph in figure 4. It highlights the part of the 
route (with lines in bold), where the GMD is in the 
visual field of the observer. 
Figure 5: The route partition in rue Xavier-Neujean 
with respect to the figure 4. 
S2
S1
S3 
S4
The part of the route which is highlighted in the 
place (S2) is one of the most important. According to 
figure 3, it is characterized by a wide opening that the 
observer sees on his right during a long time. This 
place appears suddenly. Just before, for 
approximately the same time, the observer perceives 
a narrowing between the church (S1) on his left and 
the building on his right. After in the place, the 
observer begins to see another narrowing before to 
arrive near the parking lot (S3). The effect between 
S2 and S3 is interesting. Indeed there is a kind of 
competition between these two openings. Finally, 
according to the graphs 3 and 4, the opening in S4 is 
seen directly in the visual field of the observer and 
grows gradually till the end of the route. 
With the help of the GMD radius, the GMD rear-
front location measure enables to partition a given 
route into sequences, with respect to the potential 
interest of the observer. 
5 Conclusion 
A new method to analyze urban open spaces has been 
introduced and discussed in this paper. It is based on 
the study of the sky shape variations in the visual 
field of the observer along a route. This method uses 
spherical projections and skeletonization, which 
brings a new approach to analyze the motion-
perspective. 
The measures associated with this method are 
based on the extraction of the GMD, which is the 
skeletal point with the higher distance from the 
boundary. The GMD radius is relevant to the visual 
environment of the observer. Whereas, the GMD 
rear-front location is relevant to the observer 
direction along a route. The combination of these two 
measures allows to partition urban routes into 
sequences, with respect to the potential interest of the 
observer. 
The method presented here is dedicated to the 
characterization of dense urban spaces. It gives a 
dynamic assessment of urban visual quality, and can 
help to enhance existing tools which analyze the 
urban environment location by location. 
Nevertheless, tests and improvements of the 
measures will certainly be required in order to have a 
better precision and interpretation of the method. 
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realism in city design. University of California Press, 1998. 
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English

Visual urban space assessment from sky shape analysis

Open Repository and Bibliography - Liège

International audienceMost of existing computer methods to analyse the perception of the open spaces do not consider the Gibsonian motion perspective , i.e.,the gradual changes in the visual field in the rate of displacement. This paper describes a new approach considering this motion perspective to analyse paths and routes in urban environments. Based on Teller 's spherical metric, it proposes to use the skeletonisation as a way to trace the variations of the sky shape between successive views. It is thereby possible to structure the route into sequences,according to its potential interest in the urban environment

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Visual urban space assessment from sky shape analysis

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