recovering traditions

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Recovering traditions in the digital era: the use of blimpsfor modelling the archaeological cultural heritage

Javier Gomez-Lahoz, Diego Gonzalez-Aguilera*

Cartographic and Land Engineering Department, University of Salamanca, Hornos Caleros, 50, 05003 Avila, Spain

a r t i c l e i n f o

Article history:

Received 10 July 2007

Received in revised form 1 July 2008

Accepted 29 July 2008

Keywords:

Archaeology

Low-cost aerial photogrammetry

Modelling

a b s t r a c t

Driven by progress in sensor technology, algorithms and data processing capabilities, the recording and

3D virtual modelling of complex archaeological sites is currently receiving much attention. Nevertheless,

the problem remains the huge effort and costs that have to be invested to obtain realistic models. Besides

on-site measurements, much time is often spent in manually rebuilding the whole site with a CAD

package or a 3D-modelling tool.

In this paper, a low-cost and flexible system has been devised and realized for virtual archaeological sites

modelling, starting from the acquisition system and ending with the generation of a virtual model in

three dimensions. Different efforts have been made to increase the level of automation without losing

accuracy and reliability. Finally, in order to demonstrate its capabilities some examples applied in

archaeological sites are tested and reported.

2008 Elsevier Ltd. All rights reserved.

1. Introduction

From the beginnings of photogrammetry up until now, photo-grammetry has always been a technique that has provided accurate

and reliable data in a cost effective way, assigning to the back-

ground other important aspects such as 3D visualization of the

results. In fact, the most relevant visualization examples in

a photogrammetry context were defined by a rigid support, two-

dimensional representation for a rigorous metric analysis; for

example topographic maps with both planimetric features and

contours obtained from aerial photogrammetric cases and rectified

maps of facades from close-range applications.

Fortunately, the emergence of new technologies and disciplines

has provided that the 3D visualization of large and complex sites is

currently receiving much attention. In this way, photogrammetry is

making progresses towards two directions: to popularize the

output data, that is, to make the data available to as many users andfor as many applications as possible; to popularize the technique,

that is, to make the technique readily available in a user-friendly

environment to as many non-photogrammetrist, end-users as

possible. This last goal seems to be the most demanded nowadays,

since several softwarepackages enable the end-user to obtain some

photogrammetric products. However, regarding the popularization

of the output data, 3D visualization together with virtual reality

constitue an alternative that offers promising perspectives for the

dissemination of the results. Particularly, in the archaeologistss

field, new insights can be gained by immersion in ancient worlds,

inaccessible sites can be made available to global public anddifferent periods or building phases can coexist.

The paper presents the following structure. After this intro-

duction, Section 2 briefly reports and discusses some geomatic

techniques applied to archaeological sites modelling. Section 3

explains in detail the full low-cost methodology for the recording,

modelling and virtual visualization of archaeological sites. Some

experimental applications together with a technical discussion are

reported in Section 4. A final section is devoted to sketch some

future activities and conclusions.

2. Geomatic techniques applied to archaeological

sites modelling

The fast, accurate, cheap modelling and visualization ofarchaeological sites is a trivial demand, but far from being fulfilled.

The justification is twofold. Firstly, because of its complexity, with

the presence of diverse geometric and radiometric features.

Secondly, due to its conceptual interpretation: the archaeological

drawing is first and foremost a scientific document and as such,

reproduces reality through a graphic interpretation according to

fixed rules and criteria.

Traditional methods of string grids do not provide accuracy

standards and a simple survey of the site can only provide a layout

with a few accurate points connected with vectors, without any

further information. Moreover, both methods have the disadvan-

tage of extra people working within the archaeological site for* Corresponding author.

E-mail addresses: [email protected],[email protected](D. Gonzalez-Aguilera).

Contents lists available atScienceDirect

Journal of Archaeological Science

j o u r n a l h o m e p a g e : h t t p : / / w w w . e l s e v i e r . c o m / l o c a t e / j a s

0305-4403/$ see front matter 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.jas.2008.07.013

Journal of Archaeological Science 36 (20 09) 100109
mailto:[email protected]:[email protected]:[email protected]://www.sciencedirect.com/science/journal/03054403http://www.elsevier.com/locate/jashttp://www.elsevier.com/locate/jashttp://www.sciencedirect.com/science/journal/03054403mailto:[email protected]:[email protected]


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a prolonged period of time, which increases the economical cost,

as well as the possibility of accidental destruction of important

findings.Ioannidis et al. (2000)sustain that restoration, recording,

reconstruction or even the study of an archaeological site requires

accurate plots through the use of modern surveying techniques;

more recently,Finat et al. (2005)talk about a flexible multi-input

and multi-output approach able to support the information arising

from different sensors or techniques and to provide different

levels of information to users with different requirements: from

users to experts; in the same line, El-Hakim et al. (2004) remark

on eight requirements to achieve a full modelling of complex sites:

high geometric accuracy, capture of all details, photorealism, high

automation level, low-cost, portability, application flexibility, and

model size efficiency. The order of importance of these require-

ments depends on the applications objective. As a result, the use

of laserscanning and close-range photogrammetry in archaeology

is becoming quite common due to its property of combining

metric characteristics together with a high level of radiometric

detail. However, until now end-users have been discouraged by

cost, time needed to processing data and the fact that the final

result is unmanageable.

Low-cost photogrammetric systems composed by hardware and

free software devices are the key to success to offer archaeologistsall the powerful tools for fast and accurate recording and mapping

of archaeological sites. Use of low-cost platforms for aerial

photography has been reported in many cases (Miyatsuka, 1996;

Theodoridou et al., 2000; Zischinsky et al., 2000; Karras et al., 1999;

andFaustmann and Palmer, 2005). Kites, balloons, radio controlled

model helicopters, rope-way, fish rods and well buckets are only

some of the ingenious methods photogrammetrists are using for

low altitude photography. In most of these cases, the ideal layout of

the photographs is not attained. This is reported in the case of the

radio controlled model helicopter (Tokmakidis and Skarlatos, 2002)

and in the case of the balloon (Karras et al., 1999), and generally in

any case where the photographer cannot fully control the position

(kites, balloons) or he is not physically behind the camera (rope-

way, fish rods, well buckets). Although the radio controlled heli-copter with a radio link for transmission of the imaged object on

the ground does not seem to suffer from the aforementioned

problems, this is not the case. Therefore, the scale is not equal

between photographs and overlaps are far from the ideal. Some of

these problems have been partially solved recently;Skarlatos et al.

(2004)report several good results obtained in archaeological sites

using a radio controlled model helicopter, as a semi-metric camera

platform; Eisenbeiss (2005) presents an autonomous helicopter

system that adds a GPS/INS system providing good results.

However, both approaches continue to require highly skilled

operators to control the helicopter.

The approach that is proposed in this paper overlaps with the

work done by these researchers but also differs in several ways.

Firstly, in relation to hardware or low-cost acquisition systems, anoriginal and robust camera platform that guarantees stability and

quality in taking photographs has been devised and realized

allowing the user to acquire rigorous vertical (stereoscopic) and

oblique images easily. Secondly, free software and tools have been

developed to provide virtual archaeological sites modelling.

3. Methodology

The low-cost process developed (Fig. 1) involves well-known

steps: design (sensor and network geometry); data processing

(feature extraction, image matching, image orientation, geometric

constraints and 3D points); data modelling (constrained mesh

generation, vertical walls modelling) and data visualization (virtualvisualization and mapping textures).

3.1. Design: sensor and network geometry

After some testing concerning kites and powered paragliders,

we found that blimps provided the more suitable combination of

stability and simplicity. Paramotors, besides some other drawbacks,

such as training, noise and price, are not stable enough (the same

holds forkites) to guaranteegood stereoscopic images(though they

are adequate for the less restrictive geometry of oblique images).

We found also that winds above 67 km/h (specially if variable)

were a very serious problem for the blimp to achieve the normal

case geometry and so, we had to devise a platform system that

could absorb, to a certain degree, instabilities of the wind and to

guarantee that the camera attitude would remain the samebetween two consecutive takings. This was achieved by the

combination of gyroscopes and the Picavet system described below.

So, the blimp was acquired with no further conditions but the

platform was designed and developed by us to house and shut the

camera, to allow for two degrees of freedom rotation and to

partially compensate unpredictable movements.

The designed sensor system is constituted by two main units:

Flight Unit (FU) and Ground Control Unit (GCU).

FU. The flight unit is composed of four main parts: a helium

blimp with a capacity of 11 m3 that provides a lifting force of

more than 5 kg; a Picavet system (implemented in 1912 by

Pierre L. Picavet) (Fig. 2b) which consists of a rigid frame sus-

pended, by means of a continuous string and pulleys fromanother string attached to the blimp body. The result is a rela-

tively stable self-levelling platform which maintains a fixed

inclination angle and provides effective stability of the wind

movements; a reflex digital camera Nikon D70 with a resolu-

tion of 6.1 mega pixels, a focal length of 14 mm and CCD

dimensions of 23.715.6 mm; a camera platform (Fig. 2a)

equipped with servomechanisms (endowed with gyroscopic

devices to increase stability), video and radio control which

allow us to obtain the video signal of the camera view over

a monitor in real time, as well as to control the two main

camera rotations. The most significant contribution in this

sense is an electronic gyroscope which allows us to preserve

camera rotation independently of blimp movements.

GCU. The ground control unit is composed of three main parts:a monitor, to obtain a field of view pre-visualization in real

Fig. 1. Low-cost methodology for archaeological sites modelling.

J. Gomez-Lahoz, D. Gonzalez-Aguilera / Journal of Archaeological Science 36 (2009) 100109 101


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time; remote controllers for controlling the shutting and

camera rotations (azimuth and tilt) and control ropes to

provide planimetric and altimetric control over blimp position.

On the other hand, with relation to the design of network

geometry, several assumptions have to be taken into account:

The accuracy of a network increases with the increase of the

base-to-depth (B/D) ratio and by using convergent images

rather than images with parallel optical axes.

The accuracy improves significantly with the numberof images

where a point appears. But measuring the point in more than

four images gives less significant improvement;

The accuracy increases with the number of measured points

per image. However, the increase is not significant if thegeometric configuration is strong and the measured points are

well-defined (like targets) and well distributed in the image;

The image resolution (number of pixel) influences the accuracy

of the computed object coordinates: on natural features, the

accuracy improves significantly with the image resolution,

while the improvement is less significant on well-defined large

resolved targets.

Considering the above assumptions and the sensor geometry

described above, it is advisable to plan a flight project before taking

aerial images. The optimal flight height as well as a photo-base

should be computed before taking images in order to provide the

overlap and scale required for the archaeological site modelling.

Considering the camera parameters mentioned above togetherwith an overlap of 63%, Table 1 illustrates the different

combinations between scales, flight height, base and their overlap

correspondence on the ground. sXY and sZ represent the plani-

metric and altimetric a priori deviation, respectively.

The combination underlined is usually selected as the optimal

one in that it satisfies our accuracy and efficiency requirements,

allowing cartographic scales between 1/500 and 1/1000.

After that, it only remains to compute the number of photo-

graphs and strips required to cover the area of interest (Fig. 3).

Moreover, a determined number of oblique images could be taken

to complement vertical stereoscopic images, providing relevant

geometric information, as well as contextual and panoramic

information.

Finally and before photography taking, several control points are

pre-signalized on the ground with special targets and surveyed

with GPS or total station, providing an external datum (referencesystem) of the network. At least a minimum of three points

Fig. 2. Sensors design: camera platform (a) and Picavet system (b).

Table 1

Flight project planning

Height (m) Photograph scale sXY(mm) sZ(mm) Overlap (m) Base (m)

14 1/1000 8 12.8 1515 8.7

28 1/2000 16 25.6 3030 17.4

42 1/3000 24 38.4 4545 26.1

56 1/4000 32 51.2 6060 34.8

70 1/5000 40 64 7575 43.5

82 1/6000 48 76.8 9090 52.2

96 1/7000 56 89.6 105105 60.9

112 1/8000 64 102.4 120 120 69.6

126 1/9000 72 115.2 135 135 78.3

140 1/10000 80 128 150 150 87Fig. 3. Design network geometry.

J. Gomez-Lahoz, D. Gonzalez-Aguilera / Journal of Archaeological Science 36 (2009) 100109102


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between image pairs are surveyed. This step is especially important

in archaeological sites modelling where the true prevalence ofZ-

axis direction provides a real altimetric analysis.

3.2. Data processing

Depending on the types of images captured from air (vertical or

oblique), two different processing approaches are performed.

3.2.1. Vertical images processing

A sequential process composed of several well-known steps and

computed through bundle adjustment is developed. LISA free

software (Jacobsen, 2001) was used to process vertical images.

Firstly, a feature extraction of interest points1 based on a Haralick

(1985) operator is performed. Haralick first extracts windows of

interest from the image and then computes the precise position

of the point of interest inside the selected windows. The windows

of interest are computed with a gradient operator and the normal

matrix. The point of interest is determined as the weighted centre

of gravity of all points inside the window. Secondly, a pair-to-pair

image matching process is performed using the extracted interest

points. The process returns the best match in the second image foreach interest point in the first image. At first cross-correlation is

performed between image pairs and then the results are refined

using least square matching, LSM (Grun, 1985). The point with the

biggest correlation coefficient is used as an approximation for the

matching process. The cross-correlation process uses a small

windowaround each point in the first image and tries to correlate it

against all points that areinside a search area in the adjacent image.

The search area is given considering the direction of flight sequence

and the image parallax (disparity). The final number of possible

matches depends on the threshold parameters of the LSM and on

the disparity between image pairs; usually it is around 40% of the

extracted points. The disparity threshold between the two images

is one of the most complicated parameters to select. Incorrect

disparity leads to very few correspondences (parallax parameter

smaller than the correct one) or very long computation (bigger

parameter). Sometimes when the matching process is unstable,

a filtering of false correspondences based on the disparity gradient

concept (Klette et al., 1998) is used.

Finally, once images matching is performed, the image

parameters together with the surface 3D coordinates can be

computed automatically through bundle adjustment (Triggs et al.,

2000). As adjustments observations point-feature is used. The

mathematical basis of the bundle adjustment is the collinearity

model, that is, a point in object space, its corresponding point in the

image plane and the projective centreof the camera lie on a straight

line. Photogrammetric bundle adjustment is extended with func-

tional constraints, in particular through the external datum of the

network or with restrictions derived from geometric or physical

characteristics of the network.

3.2.2. Oblique images processing

Oblique images acquired from the air are processed indepen-

dently exploiting a single image-based modelling approach (Agui-

lera and Lahoz, 2006). Therefore, this step constitutes the perfect

complement for vertical images in archaeological sites modelling,

since it provides relevant geometric information due to occlusions

or lack of features to match between images. sv3DVision (Aguilera

and Lahoz, 2006) was the software used for oblique images pro-

cessing. This free software allows us to perform a single image-

based modelling applied to a wide spectrum of objects and sites.

The single image-based modelling approach starts with a feature

extraction of straight lines based onCanny (1986)andBurns et al.

(1986)operators. Moreover, a clustering of straight lines according

to three main orthogonal directions of the scene or three vanishing

points is applied. This step is performed through a robust estimator,

RANSAC (Fischler and Bolles, 1981) that incorporates an original

slope analysis as voting criterion. This step also allows us to isolate

some relevant breaklines.

After that, an estimation of camera parameters (internal and

external) based on vanishing points and some a priori information

about the scene, that is a distance, is carried out. Therefore,

a complete camera model can be recovered following two steps, in

which internal and external parameters are estimated separately. In

the first step, the intrinsic parameters, that is, the focal length, the

principal point and the radial lens distortion, are recovered auto-

matically based on vanishing points geometry and image analysis.

The orthocentre of the triangle formed from the three vanishing

points of the three mutually orthogonal directions identifies the

principal point of the camera through the cross product of the

segments of the triangle and its heights. The focal length can be

computed afterwards as the square root of the product of the

distances from the principal point to the triangles vertices. Finally,the radial lens distortion parameters (k1, k2) are estimated by

exploiting the presence of short segments (mini-segments)

through the collinearity condition. This estimation is performed

only with line segments that satisfy the orientation constraint. In

the second step, the extrinsic parameters, that is the perspective

rotation matrix and the translation vector which describe the rigid

motion of the coordinate system fixed in the camera are estimated

in a two-pass process. Firstly, the rotation matrix (camera orien-

tation) is obtained automatically based on the correspondence

between the vanishing points and the three main object directions.

This relationship allows the cosine vectors of the optical axis to be

extracted, obtaining the three angles (axis, tilt, swing) directly.

Then, the translation vector, that is, the absolute camera pose is

estimated based on some a priori object information, for examplea distance together with a geometric constraint defined by the user.

Therefore, the reference frame for the camera pose estimation is

defined with relation to the object geometry based on a local

coordinate system. The robustness of the method depends on the

precision and reliability of vanishing points computation, so the

incorporation of robust estimators in the previous step is crucial.

Finally, in order to recover 3D information from single oblique

images and overcome the so called ill-posed2 problem, the

collinearity condition supported by geometric constraints on the

object (perpendicularity, coplanarity, parallelism, etc.) and image

invariants (distances and angles) can be used to solve the 3D

reconstruction problem from a single image (Heuvel, 1998).

3.3. Data modelling

The reconstruction of surfaces from point cloud is rather diffi-

cult in the case of archaeological sites since the data are usually

uneven and sparse. No regular surfaces such as planes, cylinders or

cones can be handled to render the site surface. In addition, the

presence of vertical walls demands a two steps approach: in the

first step, the terrain surface is modelled through conventional

triangulation procedures; in the second one, the vertical walls are

modelled from simple primitives and then blended with the

triangle mesh.

1 Interest points are locations in the images where the signal content changes,

usually in two-dimensions; they are geometrically stable under different trans-formations and present high information content.

2 For each point, we have two equations (collinearity condition) and three

unknown coordinates (X,Y,Z). Therefore the system is underdetermined and somemore assumptions need to be introduced.



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3.3.1. Surface modelling

The triangulated mesh is a powerful tool to render complex

surfaces. A collection of points with no regular distribution is

converted into the vertices of a setof irregulartriangles. Best results

are achieved when the sample is dense in points and some

geometric information (breaklines) can be observed. Nevertheless

this is not always the case as the measured points may be unor-

ganized and often noisy. Triangulation can be performed in several

ways according to the geometry of the input data, so there is a wide

spectrum of algorithms that can be applied, from the easy 2D

approaches based on Voronoi diagrams to the sophisticated 3D

approaches based on tetrahedralization methods. 3D triangulations

approaches not always provide good results, but require highly

demanding computation efforts.Thurston (1997)asserts that 2.5D

approaches require less storage space for mesh generation, being

more efficient in some cases.

In the present case, the method performed to deal with the

complexity of archaeological sites is based on a 2.5D approach of

Delaunay Triangulation (Delaunay, 1934) supported by an incre-

mental method (Bourke, 1989) and improved with robust

assumptions such as geometric and topological constraints, that is

breaklines, length of triangles and adjacency of triangles. The so

called incremental method strategy starts, as first approximation,with a unique triangle which encloses the complete point cloud.

This triangle is split in recursive sets of triangles always supported

by existing points, until every point is a vertex of the setof triangles.

This process can be significantly improved if constraints, such as

breaklines, can be added to the initial data. These features are

enforced to contain triangle edges, so none of the breaklines can be

crossed by an edge triangle. On the other hand, a topological

analysis among adjacent triangles is established based on the

number of the vertices that constitute each triangle and their

adjacent triangles. This faces topology improves whatever search

operation onto the mesh, as well as the re-triangulation if it is

required.

3.3.2. Vertical walls modellingAfter this, vertical walls are extracted semi-automatically

through image processing. The basic geometric shapes that repre-

sent vertical walls are first recovered using straight lines extracted

from images, taking advantage of constraints available from

geometric relationships such as parallelism, coplanarity and

orthogonality.

Two cases can be considered depending on the position of

vertical walls (over or under ground, that is buried):

Vertical walls over the ground are generated automatically

projecting the extracted elements over the triangulated surface

orthogonally. An interpolation over the triangulated surface is

required to solve some indetermination in the projection of

polygonal elements, as well as in the intermediate positions(Fig. 4).

Vertical walls under the ground (buried) are modelled using

a re-triangulation process (Fig. 5). Firstly, the edges and their

corresponding points (big red point) that form the upper part

of a wall are identified. Then, the set of points, connected to

these points, and placed at a lower position are determined

(red point at the lower right side of the big red point). The

original triangles constituted by these two sets of points (dash

red line) are false triangles because they do not represent the

vertical walls and so are deleted. Secondly, a new point (empty

blue point) is created for every point of the first set and placed

vertically under it. The Zof these points is obtained from theZ

of the nearest points of the secondset. Finally, twotypesof new

triangles are created: on one hand, the new points are con-

nected to the closest points of the second set to form quasi

horizontal triangles corresponding to the terrain (dash blue

horizontal line) and, on the other hand, the new points are also

connected to the first set of points to form the vertical triangles

that render the wall.

3.4. Data visualization

With relation to data visualization, a texture mapping tech-

nique, based on grey-scale or colour images, has been imple-

mented. For every surface element (surfel) on the object the

corresponding pixel element (pixel) on the image can be computed

easily through the collinearity condition if the inner and outer

camera parameters are known, as in the present case. Then, thegrey-scale or colour RGB pixel values can be transferred (or inter-

polated) to the matching surfel (and closest neighbours). Particu-

larly, this procedure is developed by means of the so called Anchor

Points method (Kraus, 1993). Only the corresponding pixel of

every vertex of the triangles is determined and the grey-scale value

or RGB value is transferred from the first to the second. Then

a linear (affine) transformation is used to interpolate the grey (RGB)

value of every surfel within every triangle from the values of the

vertices.

In this case, the visualization step is supported by the VRML

(Virtual Reality Modelling Language) standard and is performed

with the aid of the software sv3DVision. Surface geometry and

simple topology are transformed into a world of objects that

exhibit the following features:

Geometry, expressed by simple primitives (mainly boxes for

the vertical walls), or by sophisticated ones (so called Indexed

Line/Face Sets for the terrain).

Appearance, expressed by a so called material implementation

that specifies the surface response to different light sources

and also by a texture or pattern representation of the surface.

Referencing in a local or global datum defined by its origin, axis

direction and scale factor.

Metadata information, containing specific information about

the object: number of faces, number of lines, surface, length,

accuracy, etc.

Sensing activity: sensors can be attached to objects, that detect

user movements through the scene or when the user interactswith some input device.Fig. 4. Vertical walls modelling over ground.

Fig. 5. Vertical walls modelling under ground.



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Movement: the previous sensing activity can trigger differentresponses such as motion or reshaping.

Decision taking (intelligence): scripts can be implemented in

java or javascript code to analyze diverse circumstances and,

consequently, adopt the better response.

Prototyping: objects can be grouped, encapsulated and reused

and so, integrated into recursive and complex frames.

4. Experimental applications

The approach described above has been tested over two

emblematic archaeological settlements located in Castile (Spain):

the Roman city of Clunia (Burgos) and the Celtic settlement of Las

Cogotas (Avila).

Roman city of Clunia. Colonia Clunia Sulpicia, capital of Con-ventus Cluniensis in the Province of Tarraconensis in Hispania, was

the largest Roman city in the Iberian Peninsula. Situated on

a limestone plain at an altitude of 1000 m it reached an extension of

130 ha. It was founded ex-novo in the Augustean/Tiberian epoch as

a sinecism of two pre-existent celtic-iberian settlements: the celtic-

iberian nucleus of Clunioq (which gave its name to the Roman city)

and the settlement known as Arauzo de Torre. The city underwent

a remarkable economic development during the 1st and 2nd

centuries A.D. and various archaeological expeditions have brought

to light the forum, the basilica, Roman baths, an abattoir and other

blocks occupied by private houses as well as the theatre which

exploits natural features of the terrain and has been a popular

subject for prints since the 19th century.

The target was to model the part of the city known as House I.From a geometric point of view this site may be characterized as

follows:

The area to be covered with stereoscopic overlap is a square of

about 100 m100 m.

The terrain on which the house rises is almost flat. The larger

relieves are due to the excavated zones.

The house walls display a regular frame. The heights of these

walls range from only a few centimetres up to 3 m. The regu-

larity helps to identify the singular points and lines that

support the house outline but the vertical walls introduce

plenty of occlusions.

Under these conditions the following decisions were assumed. Aground sample distance (GSD) of about 4045 mm (leading to

a mapping scale of about 1/5000) would be adequate for the model.The stereoscopic height precision related to it, 70 mm, is also

adequate. Due both to the object regularity and the occluded zones,

a dense oblique image network would improve greatly the results,

both in precision and reliability and for both dimensions, planim-

etry and altimetry. Therefore, the following steps were undertaken.

The flight plan (Fig. 6) consisted of three strips with three

vertical photographs each at a flying height of 75 m. Two strips

with two photographs would have been enough to achieve simple

stereoscopic conditions but, as stated above, the occlusions related

to vertical walls made up our mind to increase photographic

coverage. In addition, oblique images from a variety of positions

were also taken. The datum was defined by the GPS observation of

12 targets (red rectangles). In addition nine bigger targets (in red

also) were placed on the terrain to ease the identification of theprojected principal points of each vertical image to be taken.

Vertical image processing was performed automatically through

image correspondences with cross-correlation and LSM (Fig. 7).

Table 2shows the statistics obtained in vertical image processing.

Vanishing point detection and computation, assuming orthog-

onality conditions between the walls, main directions, were applied

to estimate the inner and outer camera parameters. The addition of

some geometric constraints ledto a partial 3D reconstruction of the

scene, for example vertical walls. After image processing, a semi-

automated point cloud of 4700 points with several breaklines and

vertical walls was obtained. Firstly, for the conversion of the point

cloud to a triangular surface mesh, a 2.5D constrained Delaunay

triangulation was applied (Fig. 8) considering only surface break-

lines. Secondly, an automated vertical walls modelling using simple

elements like polygonal boxes was applied projecting the extracted

elements over the triangulated surface orthogonally (Fig. 8).

Occluded areas were complemented and reconstructed semi-

automatically through oblique image-based modelling.

Finally, a full textured model is generated (Fig. 9). Six vertical

images were used to map textures over the surface while four

Table 2

Number of extracted points and achieved theoretical precisions s

Images per strip 3

Tie points 531

Points in two images 330

Points in three images 201

sX(m) 0.03

sY(m) 0.02

sZ(m) 0.04

Fig. 6. Design step: network geometry.



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oblique images applied texture to vertical walls. Nevertheless, in

both cases the texture mapping was applied through the Anchor

Points method considering previously a visibility surfacedetermination.

Celtic settlement of Las Cogotas. Las Cogotas is another large

site (15 ha), defended by two walled enclosures. The last excava-

tions carried out in the southwest of the second enclosure ( Ruiz-

Zapatero and Alvarez-Sanchs, 1995) have revealed an area with

abundant archaeological material and various specialized areas;

a large communal midden, a stone pavement of complex inter-

pretation connected with the fortification wall, and a potters

workshop. The wheel-made pots with painted decoration found at

the site are dated to the second century BC. The observed stratig-

raphy is also important: the existence of a midden under the walldemonstrates that before the potters workshop and the fortifica-

tions were built, artisanal activities were already being carried out

in this area.

In this case, the main target, the modelling of the main and

central part of the Celtic settlement was enriched with the goal of

the idealized reconstruction of some of the original houses on the

virtual model. From a geometric point of view this target may be

characterized in the following way:

Fig. 7. Stereoscopic pair processing through matching technique.

Fig. 8. Surface modelling through Delaunay triangulation and vertical walls modeling.



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The area to be covered with stereoscopic overlap can be

enclosed in a rectangle of about 120 m 220 m. The settlement lies on a rocky hill and therefore, the terrain is

rather complicated. The slopes are larger at the outer part of

the walls. It must be highlighted that the reconstruction of the

original houses location is highly based on breaklines that can

be clearly appreciated on the place and thus, that must be

precisely modelized.

The walls have been partially rebuilt and exhibit a curved

display.

Under these conditions a GSD of about 2530 cm (correspond-

ing to a mapping scale of about 1/3500) with a related height

precision of 45 mm was decided. Therefore, the following steps

were undertaken:

The flight plan (Fig.10) consisted of three strips with six verticalphotographs each, at a flying height of 50 m. In addition, oblique

images from a variety of positions were also taken to obtain rele-

vant information of the vertical sides of the wall. The datum was

defined by the GPS observation of 15 targets (red rectangles). In

addition,18 biggertargets (in redalso) were placedon the terrain to

ease the identification of the projected principal points of each

vertical image to be taken.

Vertical image processing was performed automatically through

image correspondences with cross-correlation and LSM (Fig. 11).

Table 3shows the statistics obtained in vertical image processing.

Oblique image processing through single image-based model-

ling performs as a complement, providing some relevant geometricinformation in the two walled enclosures such as geometric

constraints (green planes) (Fig. 12).

After image processing, a semi-automated point cloud of 7500

points with several breaklines and vertical walls was obtained.

Firstly, for the conversion of the point cloud to a triangular surface

mesh, a 2.5D constrained Delaunay triangulation was applied

considering only surface breaklines. Secondly, an automated

vertical walls modelling using simple elements like polygonal

boxes was applied projecting the extracted elements over the

triangulated surface orthogonally. Occluded areas were com-

plemented and reconstructed semi-automatically through oblique

image-based modelling.

Vertical images were used to render the main surface while

oblique images were used to render the vertical walls. In additionseveral Celtic houses were reconstructed and placed on the terrain

following the historical data available (Fig. 13).

4.1. Technical discussion

In this section the final results are discussed, as well as the

different considerations of the proposed process.

Regarding the data capture we can remark that the wind in the

case of Clunia, about 810 km/h, made it difficult to navigate the

blimp and led to additional time (about 50%) devoted to this

purpose. In the case of Las Cogotas the difficulties arose from the

natural conditions of the site: steep slopes, huge granite rocks, trees

and tall weeds. This hindered considerably the task, not only for the

difficulty of moving around the place carrying the blimp, control-ling the camera set and measuring the targets, but also because of

the difficulty of placing with enough accuracy (less than 3 m) the

projected principal points of the vertical images. Very probably this

task can be improved, whenever these natural conditions are

found, if the coordinates of these points are computed previously

and then, the GPS is used to find the targeted location. Note that the

regular disposition of the walls in Clunia facilitated greatly this

work.

In relation to data processing, the most remarkable aspects to be

discussed are relative to the automatic reconstruction of the walls.

In the case of Clunia, these difficulties concern the points (corners)

where orthogonal walls met. Additional manual work was needed

to identify these points accurately beforethe upperpart of thewalls

was projected on the ground. Further developments should aim atthe automation of this item. In the case of Las Cogotas the most

Fig. 9. Textured and virtual 3D model.

Fig. 10. Design step: network geometry.



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crucial aspects are related to the curved nature of the walls. In thiscase, also, some editing work was needed to cluster adequately

isolated segments before the wall generation step.

Finally, concerning the data visualization, it should highlight the

qualitative change that represents the inclusion of houses in the

virtual model. This issue not only requires special care for the

material implementation of roofs, walls, doors, etc. It implies a new

visualization concept which incorporates time as an extra dimen-

sion towards the 4D worlds.

5. Conclusions and future perspectives

The presented paper has investigated and tested the viability

and possibilities opened by the use of blimps for archaeologicalsites modelling. A consistent and reliable full process pipeline has

been developed and presented. It was demonstrated with different

practical examples tested through our own tools and software.

In relation to the most relevant aspects of the proposed

approach, we could remark the following:

The development of the aerial low-cost platform constitutesa non-destructive technique for archaeological sites modelling

that enables the access to the geometry (shape, size and

dimensions) and radiometry (texture and material) of the

object even if it is inaccessible. The flowline implemented represents an original alternative to

get low-cost aerial orthophotos with large mapping scales.

The final results have been well accepted by professional

archaeologists, since the usually obtained metric measure-

ments are based on classical approaches which are whether

limited in accuracy or time consuming. Particularly, the accu-

racy attained with this approach is around 0.03 m for (X,Y)

coordinates and 0.05 m for Z coordinates.

Fig. 11. Stereoscopic pair processing through matching technique.

Table 3

Number of extracted points and achieved theoretical precisions s

Images per strip 5

Tie points 337

Points in two images 225

Points in three images 112

sX(m) 0.01

sY(m) 0.03

sZ(m) 0.05Fig. 12. Geometric constraints (planes) applied to walled enclosure modeling.



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The virtual 3D model has enabled archaeologists to extract

whatever metric measurement interactively, as well as to get

remote views from whatever viewpoint.

With relation to the field work and its logistic, the initial oper-

ation of inflating the balloon andputting it in place consumesabout

4560 min and after this, the operations of changing the batteries

or downloading the images from the memory card are somewhat

fast (no more than ten minutes). So the larger the ground area, the

more cost efficient is the methodology, reaching a productivity of

3 ha/h approximately. The system is simple enough to be handled

by two persons: one to drive the blimp and another one to control

the image taking.

Finally, regarding future perspectives, the 3D virtual model

provides for free and flexible travel in space, but does not provide

a travelling in time. Therefore, the development of a process able to

analyze and superpose the excavations for every stratigraphic layer

would allow us to dispose of its evolution over time. Another mid-

term challenge could be the adaptation of this low-cost system to

remote sensing applications through new filters (ultraviolet and

infrared) incorporated into the camera platform, so multispectral

images could provide relevant information about the location and

classification of occluded areas.

Acknowledgements

The authors would like to thank Amaya Rubio and Henar Zapico

who, as students at Salamanca University, supported and put in

practice some of the results and tools obtained at the Las Cogotas

and Clunia settlements.

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Fig. 13. Textured and virtual 3D model with the ideal reconstruction of Celtic houses.


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