GIScience Integrated with Computer Vision for the Interpretation
and Analysis of Old Paintings
Motti Zohar
1a
, Ilan Shimshoni
2b
and Fadi Khateb
2
1
Department of Geography and Environmental Studies, University of Haifa, Israel
2
Department of Information Systems, University of Haifa, Israel
Keywords: GIScience, Computer Vision, Geo-referencing, Cartography, Jerusalem.
Abstract: Photographs of Ottoman Palestine are available only from the 2
nd
half of the 19
th
century onward. Thus, in
order to reconstruct the landscape at the time one should rely on other visual sources such as old paintings.
To do so, their accuracy and completeness must be addressed first. In this paper we analyse a painting from
1823 by the British Sir Frederick Henniker that drew the Old City of Jerusalem when standing somewhere on
the Mount of Olives. We use GIScience techniques with computer vision capabilities to resolve the exact
location where the artist stood as well as verifying errors and completeness of the painting. Preliminary results
demonstrate that the location of the artist when drawing the painting was on top of Mount of Olives (close to
present-day 7 arches hotel) rather at the Cave of the Apostles as cited in the NLI librarian citation. Additionally,
the accuracy of his work was verified by comparing the features he drew on the canvas to their actual location
on a present-day photograph.
1 INTRODUCTION
In 1820 the British traveller, Sir Frederick Henniker
(1793-1825), visited Ottoman Palestine (Ben-Arieh,
1997, p. 30). In his book from 1823 he presented a
painting of the Old City of Jerusalem alleged to be
drawn from the Cave of the Apostles located at the
western slope of the Mount of Olives (Henniker,
1823). Henniker portrayed the Old City surrounded
by the 16
th
century Ottoman walls along with other
prominent structures and monuments, some are still
standing till present-day (Fig. 1). Photography in
Palestine was not available until the 2
nd
half of the 19
th
century (Nir, 1985). Thus, Henniker’s painting as
well as paintings of other artists (Röhricht, 1890) may
serve as a meaningful resource for past landscape
reconstructions in periods prior to the mid-19
th
century. However, relying on historical paintings
must be done carefully whereas they might be
inaccurate, incomplete and subjected to the artist’s
perception and conceptualization of reality.
a
https://orcid.org/0000-0002-6567-7296
b
https://orcid.org/0000-0002-5276-0242
Figure 1: (a) The Old City of Jerusalem photographed from
the Mount of Olives (photographing: Motti Zohar, January
2020). Prominent features are noted in black on the
photograph e.g., E (Al Aqsa mosque), L (Dome of the
Rock), M (Church of the Holy Sepulchre) and Q (the
Custodia Terra Sancta); (b) The painting of Henniker from
1823. See the complete list of notations in Table 1 in the
Appendix.
Zohar, M., Shimshoni, I. and Khateb, F.
GIScience Integrated with Computer Vision for the Interpretation and Analysis of Old Paintings.
DOI: 10.5220/0009464902330239
In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 233-239
ISBN: 978-989-758-425-1
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
233
2 GIScience AND COMPUTER
VISION
The rapid development of GIS (Geographic
Information Systems) and GIScience (Geographic
Information Science) during the last few decades
have revolutionized the way scientists manage and
analyse spatial data (Goodchild, 2010). With
quantitative and qualitative capabilities (Cope &
Elwood, 2009; Y. Wang & Taylor, 2018) GIS serves
a wide range of spatial applications (Sui, 2015) and
forms a proper platform to verify spatial hypotheses.
As far as cartography is concerned, GIScience
enables spatial analyses (Levin, 2006; Schaffer et al.,
2016; Zohar, 2019); the creation of deep maps as
multimedia conveyors of places and their everyday
lives (Bodenhamer et al., 2015); and the inspection of
narratives using map stories (Antoniou et al., 2018;
Mennis et al., 2013). In historical-based studies,
GIScience is used in creating new forms of virtual
knowledge (Gregory & Healy, 2007; Knowles,
2008); performing 2D and 3D landscape
reconstructions (e.g., Davie & Frumin, 2007;
Georgoula et al., 2013; Nakaya et al., 2010;
Rubinowicz & Czyńska, 2015; Zohar, 2017); and
resolving complex scenarios of past phenomena (e.g.,
Bender et al., 2005; Katz & Crouvi, 2007; Verhagen
& Jeneson, 2012).
Like GIS, computer vision and especially
machine learning based computer vision have
undergone major advances over the last two decades
enriching also the GIS capabilities and automating
tedious processes such as feature extraction and
digitization (e.g., Chen et al., 2018; Fanos et al., 2018;
Garosi et al., 2019; Naghibi et al., 2016). Methods can
therefore be applied and developed for addressing
problems of interpretation of historical visual sources.
These include methods for registration of
photographs as an addition to geo-referencing maps
(Aubry et al., 2014; Goshen & Shimshoni, 2008;
Hartley & Zisserman, 2003). Once a given source has
been aligned and registered, the use of machine
learning may assist in extracting and aligning features
such as roads and buildings (Duan et al., 2017; Uhl et
al., 2017; J. Wang et al., 2015).
The goal of the study is to interpret the old
painting of Henniker using GIScience approaches
integrated with computation vision capabilities. In
this short paper we attempt to address the following
two questions: (1) What used to be the location of the
artist when he drew the painting? (2) What are the
errors made by the artist in comparison to actual view
of Jerusalem? We hypothesize that computer vision
capabilities may empower and semi-automate the
GIScience approaches initiated so far in analysing old
paintings during the processes of geo-referencing,
digitization and errors verification. Accordingly, we
present preliminary results of the implemented
methodology aimed at addressing these problems of
interpreting old paintings.
3 METHODOLOGY
3.1 The GIS Environment
We use the ESRI
©
ArcGIS Pro software as our base
platform for the analysis and the Israel Transverse
Mercator (ITM) (Mugnier, 2000) as the preferred
Coordinate Reference System (CRS). For coding we
use Python which can be run both internally within
the ArcGIS Pro suit and by an external IDE
(Integrated Development Environment). The
geospatial data we have used as GIS layers for our
analysis is (1) present-day orthophoto of Jerusalem
with a resolution of 0.35 meter/pixel; (2) The mid-19
th
century Jerusalem map of Wilson (1865); and (3)
Digital Elevation Model (DEM) of the Advanced
Land Observation Satellite–Phased Array type L-
band Synthetic Aperture Radar (ALOS-PALSAR)
with a resolution of 12.5 meter/pixel
(http://www.eorc.jaxa.jp/ALOS/en/about/palsar.htm)
downloaded from https://vertex.daac.asf.alaska.edu/#
3.2 3D Features as Control Points
We have noted as control points 22 prominent
features appearing in the painting of Henniker as well
as in the map of Wilson and still exist till present-day
(Figures 1, 2 and Table 1 in the Appendix). For each
feature, we have extracted the parameters of
longitude, latitude and absolute elevation (above sea
level) using the DEM. Additionally, we have
calculated the height above surface of each feature
using measurements taken in field and also those
made by Alud and Hillenbrand (2000). Given the
orientation of the painting as looking from the Mount
of Olives from east to west towards the Old City of
Jerusalem (Fig. 1), a study area of nearly 1 square
kilometre, elongated south to north, was delineated
along the ridge of Mount of Olives (Fig 2) as a
plausible area in which the artist might have stood
while he was completing his work.
3.3 Line of Sight
We assume the artist was painting what he was able
to observe. In other words, the 22 noted features were
GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management
234
probably visible to him from the location of drawing.
Accordingly, the delineated study area was gridded
into cells of 0.25*0.25 meters, resulting in 158412
potential observation points along the Mount of
Olives. Then, a visibility line of sight for each of the
potential observation point was evaluated. That is,
how many of the 22 noted features can be seen from
each of the observation points. The visibility
evaluation took into consideration a 6-8 m height of
the surrounding Ottoman walls as sight obstacles and
an offset of 2 meters above the surface as the observer
height. The frequency of the visible features from the
observation points that are within the delineated study
area is presented in Figure 3. Accordingly, the area in
which all 22 features are visible is portrayed in black
while region with limited visibility are portrayed in
purple. Not surprisingly, the visibility results are
primarily dictated by height differences, although
artificial structures such as buildings may also
influence but were omitted from the analysis.
Figure 2: Aerial photography of present-day Old City of
Jerusalem and the delineated study area on the Mount of
Olives (outlined in red). The alleged location of the artist
(Cave of the Apostles) as cited in the librarian record of the
NLI (National Library of Israel) is presented a purple
square.
3.4 Computer Vision Feature Matching
Assuming the painting is equivalent to a photograph,
a robust RANSAC (Fischler & Bolles, 1981; Hartley
& Zisserman, 2003) algorithm was implemented,
which recovered the position, orientation, and
parameters of the "camera" (the artist position). In
theory it is impossible to separate the focal length of
the camera (it’s zoom) and the distance from the
scene. Therefore, constraints on the artist’s position
were given in association with a given observation
point. This is important for verifying the horizontal
and vertical accuracy of the painting.
Figure 3: Visibility of the 22 noted features in and around
the Old City of Jerusalem. The potential observation points
where all features are visible are depicted in black.
4 PRELIMINARY RESULTS
4.1 The Location of the Artist
According to the NLI (National Library of Israel)
citation, the alleged location where the artist stood
when he drew the painting was at the Cave of the
Apostles on the western slope of Mount of Olives (at
elevation of 720 m above sea level) (Figure 2).
Following the iteration of the RANSAC model on
each of the potential observation points, it was found
that the observation point with the highest score is on
top of the mountain at coordinates 631574N,
223120E (at elevation of 794 m above sea level, close
to present-day Seven Arches Hotel) (Figure 3). The
elevation difference of 74 meters between the alleged
and calculated locations explains why the Cave of the
Apostles is not a reasonable location; had the artist
stood at this position, he could have barely seen any
of the features within the Old City. In other words, the
relative height inferiority of the Cave of the Apostles
in compare to the Old City and the surrounding
GIScience Integrated with Computer Vision for the Interpretation and Analysis of Old Paintings
235
Ottoman walls, blocks the possibility to observe any
structure located within the city. On the other hand,
our calculated location (using the RANSAC model)
confirms a clear line of sight from this observation
point to each of the 22 features as demonstrated in
Figure 1a that was photographed from the same
location.
4.2 Errors Made by the Artist
Figure 4a presents the identification of the 22 features
noted on the painting and the equivalent identification
of the RANSAC model (green and red lines), which
corresponds to the observation point with the highest
suitability score (Figure 3). For comparison, we have
implemented a RANSAC model on a present-day
photograph (Figure 4b) taken from the same position.
The results of the RANSAC models on both the
painting and photograph are used for orientation and
identifying notation errors (on the photograph) and
errors made by the artist (in the painting).
Table 1 (see Appendix) presents the differences in
pixels between the feature notations in compare to the
notations achieved by the RANSAC model. The
errors (in pixels) are listed in fields EH and EP for
Henniker’s painting and the photograph with average
error values of 56 and 51 pixels, respectively. The
errors distributions were then classified into quartiles
and the those included in 4th quartile were inspected.
Accordingly, the features with higher error values are
T, U and of lesser extent G and O on Henniker’s
painting and F and R on the present-day photograph
(Table 1 in the Appendix). The prominent high
elevated feature Q (the Custodia Terra Sancta) on the
present-day photograph was not detected on
Henniker’s’ painting; perhaps it may explain an error
of the artist when portraying a none-identified
minaret at the northwestern corner of the Old City.
The errors of features T, U and O are 195, 196 and 89
pixels, respectively. These features are located at the
north of the city (on the right wing of the painting)
imply that the artist may have overestimated lengths
at the northern region which offsets his vision.
Clearly the center of the city was more visible to the
artist thus enabling better approximation of lengths
from the location he was standing when drawing the
painting (Figure 3). The slight errors of the RANSAC
model on the present-day photograph may result from
potential distortion of the focal lens and will be
verified during the next stages of the study. Overall,
the average error of the Henniker painting is slightly
bigger than that of the photograph (56 pixels in
comparison to 51 pixels) implying that the artist was
quite accurate when drawing the scene and the
included prominent features.
5 CONCLUSIONS
In this paper we present preliminary results of a
methodology combining GIScience and computer
vision capabilities to cope with the inaccuracy
associated with historical visual sources such as old
paintings. The methodology used is at its first stages
and is being currently further developed. Yet, it
enables already at this point to track the position of
the artist when he drew the painting thus
contradicting the alleged location to be at the Cave of
the Apostles as cited in the painting’s library record
at the NLI. We have also managed to verify the
accuracy of features on the painting’s canvas, perhaps
drew by the artist with less accuracy due to sharp
offset from the location he was standing or in order to
meet his subjective perception. Additionally, we were
able to identify errors on the painting in compare to
present-day photograph by comparing the two sets of
results. Yet, to this point there are some vague
identification in Henniker’s painting which we could
have not resolved by the RANSAC model such as the
Custodia Terra Sancta location and the anonymous
minaret at the north section of the Old City.
The potential contribution of establishing
complete framework for analysing old paintings and
illustrations is enormous for historical-based studies.
In the next stages we will test additional paintings and
attempt to spatially register the included features
within a known Coordinate Reference System (CRS).
We anticipate this process will re-project the features
on the canvas of the painting back to the GIS
framework. We will test our results with past and
present-day photographs.
GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management
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Figure 4: Noted features on Henniker’s painting (a) and present-day photograph (b) The lines represent the RANSAC
identification whereas green lines denote small-scale error detection while red lines stand for relatively large errors. For
complete results see also Table 1 in the Appendix.
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APPENDIX
Table 1: RANSAC model results for identifying features appearing in the painting of Henniker. Fields of the table: S - feature
notation; Name – the name of the feature; H the height of the feature above surface; X, Y, Z - longitude, latitude and
elevation above sea level of the features, respectively; pXH, pYH - pixel coordinates of human notation on Henniker’s
paintings; rXH, rYH - pixel coordinates of the RANSAC model on Henniker’s painting; EH – pixel length difference
between human and model notation. The biggest differences of features T, U and of lesser extent G and O are noted in bold;
pXP, pYP - pixel coordinates of human notation on the photograph; rXP, rYP - pixel coordinates of the RANSAC model
on the photograph; EP pixel length difference between human and model notation. The biggest differences of features F
and R are noted in bold. Summary values of A (average), M (Median), Mi (Min), Mx (Max) and 1Q, 2Q, 3Q (quartiles
thresholds) are also listed.
S Name H X Y Z pXH pYH rXH rYH EH pXP pYP rXP rYP EP
A AlNabiDau'd 12221795 631010 795 283 213 288 159 54 
B Wall 10222186 631291 751 637 341 582 288 76 236 1580 88 1518 16
C OmarCaliph 15221987 631380 787 754 227 728 200 38 832 1496 811 1450 51
D AlQalaa' 15221668 631431 793 832 173 809 203 38 1091 1463 1083 1445 19
E AlAqsa 15222414 631468 752 857 287 824 311 4 1007 1582 1000 1586 8
F Davidcitadel 15221666 631525 796 929 183 896 203 38 1198 1478 1352 1454 156
G Wall 10222100 631198 753 396 315 473 271 89 
H AlFaqriah 15222315 631620 764 1043 239 1064 278 44 1794 1554 1832 1559 38
I AlOmar 18221835 631681 786 1056 198 1050 227 29 1819 1499 1810 1490 13
JGoldenGate 8 222559 631809 750 1450 363 1455 354 2 3086 1722 3113 1684 47
K Wall 10222594 631489 726 858 376 859 425 49 1022 1771 1020 1788 17
L HarmAlSharif 20222382 631702 768 1158 226 1215 276 76 2201 1520 2252 1573 74
M HolySepulchre 20221839 631752 792 1105 207 1120 219 19 1978 1500 1996 1491 20
N BabAlAsbat 12222482 631943 756 1661 307 1585 332 8 3519 1618 3425 1655 12
O Wall 10222511 632334 772 2166 389 2124 312 88 
P AlMalla'wiyah 8 222194 631960 778 1433 261 1437 263 4 2955 1592 2878 1562 82
Q
CustodiaT.S. 20221620 631859 807 1145 195 2053 1423 2047 1471 49
R Lion'sGate 8 222538 632020 756 1795 379 1742 342 64 4010 1717 3842 1677 174
S SheichReichan 10222189 632065 786 1608 237 1558 249 52 
T Wall 10222036 632200 794 1784 255 1589 239 195 3154 1528 3198 1547 48
U Herod'sGate 8 222224 632253 785 1942 321 1755 262 196 
V SantaAnna 12222495 632081 762 1795 341 1788 324 19 
  
 A 56 A 51
 M 44 M 43
 Mi 2 Mi 8
 1Q 19 1Q 17
 2Q 41 2Q 43
 3Q 67 3Q 57
 Mx 196 Mx 174
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