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URBAN LAND COVER MAPPING USING HYPERSPECTRAL AND MULTISPECTRAL VHRSENSORS: SPATIAL VERSUS SPECTRAL RESOLUTION
Fabio DELL’ACQUA, Paolo GAMBA, Gianni LISINI
Department of Electronics, University of Pavia, ITALY�fabio.dellacqua, paolo.gamba, gianni.lisini�@unipv.it
Working Group III/5
KEY WORDS: Urban remote sensing, hyperspectral imaging, very high resolution sensors.
ABSTRACT
In this paper a discussion about the effects of very high resolution (VHR) in both the spatial and the spectral dimensionsfor urban land cover mapping is proposed. We confirm that VHR spatial sensors are unable to discriminate between someurban materials, since they often come from the same chemical family. However, VHR in the spectral sense sometimescarries too much information, since it differentiates covers made by the same material but with different age or illuminationconditions. Finally, after out test with a supervised classifier, we stress the importance to use the context for a moreaccurate mapping, and offer one simple way to improve the classification using a priori knowledge about the geometricconstraints of the segmented map.
1 INTRODUCTION
Land cover mapping (and consequently land use mapping)in urban areas relies, at the level of detail required by mosturban planners, on very high spatial resolution (VHR) sen-sor. Recently, the availability or the development of newairborne and satellite sensors with very high spectral reso-lution allowed the comparison of data sets from these dif-ferent sources for urban material detection, recognition andcharacterization.
Indeed, hyperspectral data have been used for many urbanapplications. Urban material characterization Marino et al.(2001) has been considered by means of the MIVIS sensor,allowing not only to provide a precise definition of the roofmaterials, but also to build a database of dangerous rooftops for subsequent substitutionand destruction. Similarly,vegetation canopies and tree in urban forests provide in-formation extremely interesting for energy exchange anal-ysis, air quality (micro-climate) and hydrology. Their dis-tribution, height, stress and species have been already de-rived from low altitude AVIRIS measurements in Xiao etal. (1999). As a further example, detection of land coverand sealed parts in an urban area can be accomplished us-ing the multi-technique approach proposed in Heiden etal. (2003), where HyMap data have been analyzed for abetter characterization of urban environments. Finally, adetailed analysis of the spectral properties required by sen-sors to obtain interesting results in urban areas is given inHerold et al. (2003), where it was shown that AVIRIS’sfine sampling of the electromagnetic spectrum provides amuch better result than multi-spectral Ikonos (simulated)data, because the latter lack information on significant por-tion of the infrared wavelength range. However, no discus-sion of the spatial resolution issue is provided in the pa-per, since Ikonos is simulated at the same resolution thanAVIRIS.
One final point to be discussed is therefore if the combina-tion of spatial and spectral VHR leads to significantly bet-
ter results than one of the two characteristics taken alone,and to which extent, if any. So, this paper is devoted to thecomparison of two different hyperspectral data sets, com-ing from the ROSIS and DAIS sensors, and a Quickbirdimage.
The two hyperspectral sensors provide the best spatial avail-able up to date (2.6 and 1.2 m posting, respectively), whileQuickbird mounts the finest multi-spectral satellite sensoravailable (2.8 m). The aim of this paper is therefore toimprove the knowledge of the present possibility for ur-ban land cover mapping right now, considering the effectof both the spectral and the spatial properties of the data.Moreover, it may provide useful information for future im-plementation and choice in sensor design targeted to urbanapplications.
2 NEIGHBOURHOOD-AWARE SPECTRAL CLAS-SIFICATION
The classification tool used for comparison is based on thetechnique recently developed in Gamba et al. (2003) formulti-classification fusion of very fine spatial and spectraldata. A simple scheme of the processing steps is as fol-lows:
� a feature extraction procedure is applied to the data inorder to reduce dimensionality;
� supervised classifications are performed, comparingthe results of different spectral and spatial classifiers;
� all the classification maps are combined using major-ity voting or Linear/Logarithmic Opinion Pools;
� a further geometric refinement step is introduced, andspatial information from the neighborhood of eachpixel is taken into account to improve the results.
(a)
(b)
(c)
Figu
re1:
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tare
a:a
part
ofth
eto
wn
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aged
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seco
lors
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the
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IS(a
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OSI
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ckbi
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eor
igin
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orm
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-bi
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rdif
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ors.
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emel
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sing
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ysi
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rmap
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ew
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ajor
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rdpr
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ides
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ion
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xel
isas
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edto
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sto
whi
chit
belo
ngs
inth
em
ajor
ityof
the
map
sto
bejo
intly
cons
ider
ed.T
heL
in-
ear
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nion
Pool
(LO
P)an
dth
elo
gari
thm
icop
inio
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ol(L
OG
P)pr
oced
ures
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ire
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ead
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aini
ngse
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hich
inhe
rent
lyde
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the
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utcl
asse
sto
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nsid
ered
inth
efin
alcl
assi
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ion
map
.Fo
ran
ypo
ssib
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ttern
ofth
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tput
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ses
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etr
aini
ngse
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mpu
te����
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obab
ility
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thcl
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ngse
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ciat
edw
ithth
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lecl
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nally
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dim
ages
,an
d �
the
wei
ght
of
shadowshadowbricks
bricks
trees
trees
meadow
meadowasphalt
asphalt
(a)(b)
Figure 2: Spectra of some covers in DAIS data (a), and in Quickbird data (b).
the �-th map (default is � � �). Of course, there could besome patterns that are not present in the training set. Forthese patterns, the output class assignment follows major-ity voting.
The combined maps come from different classifiers, somebased on spectral information (Maximum Likelihood, FisherLinear Discriminant, Fuzzy ARTMAP), others on spatialinformation also (ECHO, Fuzzy ARTMAP with spatial re-classification). The last classifier is a refined version of thefuzzy ARTMAP classification chain introduced in Gambaand Houshmand (2001); Gamba and Dell’Acqua (2003).The output of a pixel-by-pixel classification is used by asecond classifier whose input is a vector representing themapping class percentages in a window around each pixelplus a random value to enhance their discriminability.
After multiple map combination, we applied a further re-classification step, again by means of the fuzzy ARTMAPclassifier, which usually provide better and more preciseresults, since it corrects random errors due to randomlycorrelated noise, i. e. errors, in the maps.
Even if the methodology has been fully presented in previ-ous papers, the usefulness is this research is in comparingthe best results on the same test area and the same test andtraining sets for different data sets. An accurate test on thespectral and spatial properties of the training sample mayhelp not only to understand the different performances, butalso to extrapolate, beyond the particular class legend weused, which may be the best choice for urban land covermapping with these sensors.
Moreover, in this paper we introduce a final step after theabove mentioned classification chain, aimed at a furthergeometric refinement of the classification. This step isbased on the usual methodology applied to panchromaticVHR images, and based on the experience gained withphotogrammetric data processing. Indeed, as correctly statedin recent review papers, (see Guindon (2001) and Gamba et
al. (2003), for instance), that it is time now to start consid-ering that the photogrammetric-based and the classification-based views should be integrated into a general framework.One possible idea, which is somehow a generalization ofJin and Davis (2004), is to match a top-down segmentationstep with a bottom up approach based on geometric fea-tures and cues grouping. In the top-down step, an initialpartitioning of areas of interest within a scene is accom-plished using simple supervised classification schemes. Areduced resolution image may be considered, and texturalfeatures may be used to complement the original informa-tion. In the bottom-up step, each part of the scene andits surroundings are searched for basic patterns of covers,and corrections to misclassifications are made based on thecontext. Alternatively, geometric features are extracted torecognize objects instead of classification segments, andthe class of each object is chosen by majority voting.
Following these line guides, in this work we apply a post-processing step to the classified land cover map, aimed atexploiting some of the constraints for urban covers. Forinstance, we may assume that roof cover pixel belong togeometric structures delimited by orthogonal edges. Thisassumptions leads to a correction procedure that searchesfor segmented regions belonging to a roof cover class andextracts their boundary. Successively, these boundaries arematched with a piecewise linear path with 90Æ angles. Fi-nally, the corresponding regions are re-drawn on the origi-nal map.
3 EXPERIMENTAL RESULTS
We offer results about the town of Pavia, Northern Italy.Four DAIS and ROSIS flight lines over the area, kindlyprovided by the German Space Agency in the frameworkof the HySens project, cover the town and its immediatesurroundings. The Digital Airborne Imaging Spectrometer(DAIS) is a multi-band systems with 80 bands in the visi-ble and infrared wavelength range (from 0.4 to 12.6 �m),
(a)
(b)
(c)
Figu
re3:
Seco
ndte
star
ea:
the
Eng
inee
ring
Scho
ol:
clas
sific
atio
nm
aps
obta
ined
usin
gth
eD
AIS
(a),
RO
SIS
(b)
and
Qui
ckbi
rd(c
)da
ta.
whi
leth
eR
eflec
tive
Opt
ics
Syst
emIm
agin
gSp
ectr
omet
er(R
OSI
S)is
am
ulti-
band
sens
orfo
cuse
don
lyon
visi
ble
and
near
infr
ared
freq
uenc
yba
nds.
The
latte
rcom
pris
es32
fre-
quen
cyba
nds
from
0.45
to0.
85�
m,s
how
ing
high
erpo
ten-
tialf
orve
geta
tion
and
gree
nar
eam
appi
ngth
anth
efo
rmer
one,
whi
ch,
onth
eco
ntra
ry,
has
abr
oade
rra
nge
ofap
-pl
icat
ions
,sin
ceit
prov
ides
info
rmat
ion
upto
the
ther
mal
infr
ared
.To
thes
eda
tase
tsw
ead
ded
elab
orat
ions
mad
eon
aQ
uick
bird
panc
hrom
atic
and
mul
ti-sp
ectr
alim
ages
(4ba
nds,
visi
ble
and
near
infr
ared
),ac
quir
edin
the
sam
epe
-ri
od(J
uly
2002
).
The
fine
spec
tral
reso
lutio
nof
som
eof
thes
eda
tase
tsco
u-pl
esw
ell
with
ave
ryhi
ghsp
atia
lre
solu
tion,
from
2.6
mfo
rD
AIS
to1.
2m
for
RO
SIS
to0.
7m
for
pan-
shar
pene
dQ
uick
bird
data
.
Inth
eur
ban
test
area
ofin
tere
st,t
hefo
llow
ing
resu
ltssh
owan
dco
mpa
real
gori
thm
perf
orm
ance
son
two
diff
eren
ttes
tar
eas,
one
inth
ece
nter
ofth
eto
wn,
cons
ider
ing
typi
calu
r-ba
nla
ndco
vers
,an
don
ein
the
surr
ound
ings
,whe
rebo
thur
ban
and
rura
lco
vers
exis
t.T
his
choi
ceal
low
san
alyz
-in
gth
eov
eral
lan
dcl
ass
accu
racy
valu
esw
ithre
spec
tto
the
spec
tral
and
spat
ialp
rope
rtie
sof
diff
eren
tleg
ends
,and
also
tofo
cus
onth
eef
fect
sdu
eto
typi
calp
robl
emsi
nde
nse
urba
nar
ea,
such
asm
aski
ngby
shad
ows.
The
first
area
isde
pict
edin
fig.1
:pl
ease
note
that
the
area
sar
eno
texa
ctly
the
sam
e,du
eto
diff
eren
ces
inth
efig
htlin
es.
For
exam
-pl
e,du
eto
the
finer
spat
ial
reso
lutio
nan
dth
esu
bseq
uent
redu
ced
swat
hof
the
RO
SIS
sens
or,
only
two
part
sof
the
scen
ear
esh
own
inth
eR
OSI
Sfa
lse
colo
rim
age,
whi
chbe
long
totw
ono
n-ov
erla
ppin
glin
esof
fligh
t.
All
the
supe
rvis
edcl
assi
fiers
used
expl
oitn
on-o
verl
appi
ngtr
aini
ngan
da
test
sets
com
pose
dby
near
ly10
0pi
xels
for
each
clas
s,an
dre
ferr
ing
to7
clas
ses
ofur
ban
cove
rs,
plus
wat
er.
Cov
ers
incl
ude
tree
s,m
eado
ws
and
bare
soil,
as-
phal
tan
dbi
tum
en,
park
ing
spec
ial
cove
ran
dbr
ick
roof
.To
disc
ard
prob
lem
sw
ithsh
adow
edar
eas,
asp
ecia
lcl
ass
for
shad
owpi
xels
was
adde
d.
The
high
esta
chie
vabl
eva
lues
fort
heov
eral
lacc
urac
yam
ong
allt
hete
sts
mad
ear
esh
own
inTa
ble
1.N
ote
that
thes
eva
l-ue
sdo
nott
ake
into
acco
untt
hefin
alge
omet
ric
refin
emen
t,si
nce
this
may
intr
oduc
eso
me
bias
inth
ere
sults
due
toits
depe
nden
ceon
spat
ial
reso
lutio
non
ly.
Thi
sis
the
reas
onw
hyit
will
bedi
scus
sed
sepa
rate
ly,
atth
een
dof
this
sec-
tion.
The
accu
racy
valu
esin
the
Tabl
esh
owth
atfo
rthi
sfir
stte
star
ea,
but
also
glob
ally
spea
king
,sp
atia
lre
-cla
ssifi
catio
nus
ing
AR
TM
AP
neur
alne
twor
ksis
byfa
rth
ebe
stop
tion.
Mor
eove
r,th
ese
valu
esst
ress
the
fact
that
high
spat
ialr
es-
olut
ion
isno
ten
ough
,an
dlo
wer
spat
ial
post
ing
isw
ell-
bala
nced
bym
ulti-
spec
tral
capa
bilit
y.A
sa
mat
ter
offa
ct,
DA
ISm
aps
are
alw
ays
bette
rth
anQ
uick
bird
ones
.M
ore-
over
,it
shou
ldbe
men
tione
dth
atin
Qui
ckbi
rdm
aps
som
eof
the
cove
rsar
eno
tac
tual
lypr
esen
t.Fo
rin
stan
ce,
bitu
-m
enan
das
phal
t,be
ing
virt
ually
indi
stin
guis
habl
edu
eto
the
low
spec
tral
reso
lutio
n,ar
ejo
intly
cons
ider
ed.
Fina
lly,
RO
SIS
map
ping
accu
racy
isba
sed
ofco
urse
onbo
thth
efin
esp
atia
land
spec
tral
reso
lutio
n.
Togi
vean
idea
ofth
edi
ffer
entp
robl
ems
com
ing
from
spa-
tiala
ndsp
ectr
alhi
ghre
solu
tion,
we
offe
rin
fig.2
the
spec
-tr
aof
five
land
cove
rsar
ound
the
area
ofth
eca
stle
ofPa
via,
asth
eyca
nbe
dedu
ced
byth
eQ
uick
bird
and
DA
ISda
tase
ts.
The
two
subs
ets
show
also
the
diff
eren
cein
spat
ial
deta
ils,a
ndth
usal
low
com
pari
ngth
egl
obal
effe
ctof
both
reso
lutio
nson
the
map
ping
capa
bilit
ies
ofth
etw
ose
nsor
s.
As
for
the
seco
ndte
star
ea,
the
Eng
inee
ring
Scho
olof
the
Uni
vers
ityof
Pavi
a,th
ela
ndco
ver
lege
ndus
edw
aspa
r-tia
llydi
ffer
ent.
We
cons
ider
ed9
cove
rs,
i.e.
tree
s,m
ead-
ows
and
bare
soil,
asph
alt
and
bitu
men
,gr
avel
,pa
rkin
gsp
ecia
lco
ver
and
met
allic
roof
s.E
ven
inth
isca
seth
elo
wer
spec
tral
reso
lutio
nof
Qui
ckbi
rdda
tadi
dno
tal
low
disc
rim
inat
ing
bitu
men
agai
nsta
spha
lt,an
dth
etw
ocl
asse
sw
ere
join
tlyco
nsid
ered
.A
gain
,ash
adow
clas
sw
asar
tifi-
cial
lyin
trod
uced
,to
redu
cem
iscl
assi
fied
pixe
ls.
The
clas
-si
ficat
ion
map
sfo
rth
isar
eas
are
show
nin
fig.3
.
Overall accuracy (%) DAIS ROSIS Quickbird
Center 97.5 99.3 91.8Engineering School 87.5 91.4 80
Class. algorithm DAIS ROSIS Quickbird
Center ARTMAP (DAFE) Spatial ARTMAP Spatial ARTMAPEngineering School Majority voting Spatial ARTMAP Spatial ARTMAP
Table 1: Comparison of the best overall classification accuracy values and classification methods for land cover mapsobtained from data by each of the three sensors and referring to the same test areas.
A look at the maps reveals that there are some misclassifi-cations and pixels assigned to the wrong (even if spectrallysimilar) class. We have, for instance, some exchanges be-tween pixels in the bitumen and gravel classes. As a mat-ter of fact, the gravel on top of some of the buildings isposed on a bitumen substrate, so that the spectral responseis somehow mixed. Similarly, bitumen and asphalt are alsoexchanged in some areas. ROSIS data show a better per-formance, but suffer from a stronger dependency on thetraining set. Pure pixels are mandatory and a few mixedspectral response may change dramatically the per-classaccuracy values.
3.1 Geometric refinement of the classified maps
As introduced above, a final step was applied to some ofthe classes. The procedure is based on a boundary extrac-tion for each of the segments of the class of interest, fol-lowed by a piece-wise linear reconstruction of this bound-ary and, finally, a step that poses constraints on the anglebetween subsequent segments. There are therefore threeparameters ruling the procedure: the maximum length ofthe segments and the maximum distance from the originalboundary determine how approximate is its piece-wise lin-ear representation. The tolerance on the angle to be “cor-rected” is used to choose which are the segments whosedirection is to be re-assigned in a clock-wise path alongthe piece-wise linear approximation.
An example of block refinement through geometric con-straints is shown in fig. 4(a). Similarly, the segments ofthe brick roof class in parts of the Quickbirdclassificationmap of the whole town, before and after the geometric cor-rection, are showed in fig. 4(b) and (c), respectively. Wemay observe an improvement in the shape of the objects,that become more recognizable. However, the shapes arenot as regular as expected, as a consequence of the impre-cise piece-wise linear representation of their boundaries.This in turn is due to the small number of pixels definingthe boundaries. We may observe more visible improve-ments when considering, in another part of the town, nearthe Engineering school, the same approach as applied tothe “road” (actually, asphalt) class, as shown in fig. 4(d)and (e). In this example the advantage of using geometri-cal constraints on the shape of the objects is more evident,and results in a regularization of their boundaries.
4 CONCLUSIONS
The present work shows that, with respect to urban andurban/rural land cover mapping, very high resolution in thespectral sense is more valuable than in the spatial sense. Itmay be better to have more bands recorded by a sensor thata more detailed image of the scene.
The main reason for this behavior is the similarity of manycovers due to their very similar chemical components. In-deed, once a sufficient spatial resolution is achieved, sothat in the image there are mostly spectrally pure pixels,the geometric properties of the objects may be improvedexploiting a priori information about their shape. This in-formation is, in fact, valuable but already available for ur-ban areas and objects herein.
Instead, the correctness of assignment of an object to theright land cover class (and possibly the right land use class)is based on the possibility to recognize the spectra of thematerials, which in turn depends on the number and posi-tions of the imaged wavelengths.
ACKNOWLEDGMENTS
The authors want to thank Francesco Grassi for his help inperforming some of the classification tests. The classifica-tions using Quickbird data were performed at the Univer-sity of Siena. Thanks to Andrea Garzelli for running thetests and providing the mapping results.
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(a)
(d)
(e)
(b)
(c)
Figu
re4:
(a)
anex
ampl
eof
geom
etri
cre
finem
ento
facl
assi
fied
segm
ento
fth
esc
ene;
the
sam
eap
proa
chis
appl
ied
toth
ese
gmen
tsin
(b)
toob
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)to
obta
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).
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ral
Net
wor
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atio
nto
plan
imet
ric
info
rmat
ion
extr
actio
nfr
omve
ryhi
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anad
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Edi
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the
The
me
Issu
eon
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ms
and
Tech
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ram
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n.1-
2,pp
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2003
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avis
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actio
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omhi
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esol
utio
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urba
nar
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gst
ruct
ural
,co
ntex
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ectr
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form
atio
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EU
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SIP
Jour
nal
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dSi
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essi
ng,
Spe-
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Issu
eon
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ance
sin
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llige
ntV
isio
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s:M
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pplic
atio
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004.