Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds, CGames ’07 (Eugene Ch’ng)
Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds
EUGENE CH’NG
School of Computing and Information Technology
University of Wolverhampton
Wulfurna Street WV1 1SB
Wolverhampton, United Kingdom
[email protected]
KEYWORDS
Archaeology, Virtual Heritage,
Engine, Vegetation Modelling
Visualisation,
Games
ABSTRACT
Research and development in graphics and games engines
has great potentials in many application areas, archaeology
being one of them. While these entertainment-driven
developments, mainly in software algorithms and enhanced
graphics hardware are being improved and being made more
accessible and at a more affordable rate, other areas
belonging to the science and engineering are gradually
discovering their use in visualisation. However, the process
that leads to the final delivery of the presentation layer of an
archaeological visualisation is often a thorough and lengthy
scientific process, involving more than just a pretty picture
on a high-tech display unit. As the statement by experienced
artists holds true – ‘the process is more important than the
outcome’, so the credibility of a final interactive
visualisation greatly depends upon the ‘underground’
process. This article explores the methodology, techniques
and strategies of an archaeological visualisation as applied to
a real-world problem – a 10,000 year old mystery landscape
at the bottom of the North Sea.
INTRODUCTION
In recent times, archaeology has arrived at the virtual
age, using digital technologies to restore, preserve, and
recreate sites and artefacts for analysis, interpretation, and
for educating concerned parties and for initiating new
research opportunities. Archaeology began as a method of
identifying places and objects already known to exist from
historical records. Archaeology as a science in the modern
times has, since 1968 very much developed towards the later
‘processual’ (scientific) approach (Binford 1968) – an
approach believing in objective science (Hodder 1999). In its
developments, it has also become a means of discovering
new facts about ages beyond the reach of written evidence.
Whilst the scientific aspects of archaeological interpretation
are indispensable in modern archaeology, researchers are
discovering that the sensual or experiential approach has
become a necessary element in particular studies (Tilley
1994). For example, Chapman and Gearey (Chapman and
Gearey 2000) discovered that there might be a potential
synergy arising from closer integration of the ‘processual’
and the sensual approach, particularly in studies related to
prehistoric sites and landscapes (Gearey and Chapman 2005).
Most archaeologists however, depended on Geographical
Information Systems (GIS) as a means of visualising datasets.
For example, Gearey and Chapman integrated the approach
in the concept of digital gardening for visualising prehistoric
landscapes. In another example, Spikins (Spikins 2000)
utilised a simple rule-based algorithm for selecting the most
probable dominant woodland type for large spatial-temporal
landscapes in order to determine the environment, population
and settlement of a period of the Mesolithic age. However,
due to the essentially 2D nature of GIS systems, aside from
limitations such as cognitive representations, temporal
analysis and three-dimensional analysis (Ebert and Singer
2004), and the accuracy of the represented model (Fyfe
2005), GIS systems are not in a suitable format for the
experiential aspects of interpreting prehistoric sites, which
required rich interactive visual displays of realistic 3D
representations of archaeological datasets. The advent of
powerful graphics engines and the toolkits for ‘modding’
levels in games engines is changing the face of such
visualisation. As a result, entire villages (Hirayu 2000), cities
(Thwaites 1998; Ennis 2000; Cremer J. 2001; Latousek 2003)
and even caves (Moore 1998) were constructed as part of a
large collection of virtual reconstruction work with the more
elaborate projects such as Virtual Reality Notre Dame
(DeLeon V. 2000) and Virtual Everglades at Florida
(DeLeon V. 1998) depended on Epic’s Unreal games engine.
These digital reconstructions have, to date, contributed
significant awareness and interest among the general public,
providing educational benefits to concerned parties and new
exploratory and interpretive research tools for archaeologists,
enabling them to explore ideas that were otherwise
impossible. For example, archaeologists could now
determine the actual size of an architectural space by ‘being
there’, they could also ‘peer into obscured artefacts and
document landscapes in great detail’, according to Bawaya
(Bayawa 2006), who also noted that Western Europe is the
most generous funder of such research.
Recreating lost worlds could have its drawbacks
however as far as interpreting a site and educating the public
regarding it via interactive displays is concerned. This is
based on a fact - archaeological sites are often incomplete.
As such, virtually reconstructing an incomplete prehistoric
site depended on accumulated knowledge and, very often,
logical assumptions of what the missing pieces are. The
process therefore is important in the justification of the
outcome. This article explores the methodology of using a
games engine for recreating an ancient river valley – a
Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds, CGames ’07 (Eugene Ch’ng)
mystery shrouding a 10,000 year old landscape submerged
under the North Sea. The paper begins with a background of
the site followed by earlier research work in section three.
Section four covers the importance of the methodology,
describing the pipeline of tools and the scientific modelling
leading to the visualisation outcome of the landscape via the
Crytek CryEngine. Finally, the paper concludes with a
summary of the research and future application areas,
including the potentials of new techniques developed during
the research.
period, a realistic and natural approach is to model each
plant as an artificial life unit, leveraging the emergence
phenomenon for plant dispersal. As such, a graphics engine,
the SeederEngine, capable of simulating a weather system
and artificial life-based vegetation was created for scientific
modelling of the landscape (Ch'ng et al. 2005; Ch'ng et al.
2005). The work presented here applies the artificial life
framework to the actual landscape by completing the
methodology required to visualise the river valley in an
interactive Virtual Environment.
SUBJECT BACKGROUND
METHODOLOGY
The research project in collaboration with the University of
Birmingham’s Institute of Archaeology and Antiquity was
initiated following the discovery of an ancient landscape in
the Southern Regions of the North Sea during oil prospecting
exercises. Initial investigation revealed a large river valley
which is part of an ancient landscape that existed during the
Mesolithic period (10,000 – 7,000 bp) before glacial melting
and rising sea levels finally submerged the terrain and
eradicated living organisms from the once habitable land
bridge. The Shotton river valley is located in the southern
North Sea basin within the Dogger Bank area, a site
historically associated with many archaeological findings.
The river valley is 600 metres wide with an observed length
of 27.5km. Figure 1 shows the reconstructed area.
The methodology defined in this work involves four
crucial phases with phase one being the most important as
work done here determines the accuracy of the rest of the
phases. The three following phases involves a pipeline of
tools, both custom and proprietary, leading to the final
outcome. Research is active throughout the phases and
where needed, developments are performed. Figure 2 is a
diagram showing the pipeline and the sections below cover
the phases in details.
Painting the Theoretical Picture
The work involved here explores the time and place of
this particular landscape by delving into past archaeological
knowledge and present findings. The study (Gaffney and
Thomson 2003; Ch'ng, Stone et al. 2004) identified earlier
perceptions of the land bridge and its settlements, geology,
cultures, diets, climates and environments, prehistoric plant
types, fauna and recent excavations of Mesolithic houses
(Waddington 2003) in Howick and East Barns together with
traces of tools, and evidence of food sources (shells of clams
and charred remains of acorns and hazel nuts). The research
gathered a vast amount of knowledge and painted a clearer,
albeit theoretical picture of what the landscape looked like
and also affirmed its value and the importance of
reconstructing it for archaeological interpretation and public
education.
Content Generation
Figure 1. Location of Shotton River Valley
PREVIOUS WORK
Pilot visualisation work (Ch'ng et al. 2004) has been carried
out using simple VR technology. Although Ch’ng et al.’s
early work depended on subject matter experts’ knowledge
and opinions (geo-archaeologists, palaeo-environmentalists,
botanists, etc) with regards to the placements of virtual
vegetation onto the 3D terrain, the distribution of plants in
this manner do not necessarily represent an accurate
formation of plants on the landscape during the Mesolithic
The content generation phase has three sections. The
first section investigates ways in which the seismic datasets
could be converted. The dataset originally gathered for oil
prospecting is derived from PGS’s 3D Mega Survey data
sampled at 25m spacing with the seismic source being a bolt
airgun.
Initial investigation of the data revealed an
interesting river valley to the north thought to possess
significant archaeological value. The seismic dataset were
added into a GIS and TGS Amira for processing with the
output as contours and 3D voxel volume for analysis.
Finally, the processed dataset was converted into polygonal
model and optimised for virtual environments. Section two
reconstructs Mesolithic houses, tools and artefacts, and
formed these elements into villages based on physical
findings in Howick and East Barns. As thousands of virtual
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Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds, CGames ’07 (Eugene Ch’ng)
Figure 2. The Phases Leading to the Outcome
plants will clothe the landscape, a variety of virtual plant
and distribution across large 3D terrains. An artificial life
representations were experimented with in section three
framework and graphics engine was developed using
before determining the most realistic versions for use in the
DirectX 9.0c for the simulation test bed. Based on botanical
real-time rendering environment.
research, prehistoric plant properties (XML-based) and
behavioural rules (equations and algorithms) are distilled
into programming procedures. The following rules defined
Modelling and Simulation
the behaviours of the plants:
This phase represents the core scientific activity for the
project with four sections – Framework development,
artificial life modelling, experiments and applications of the
research to the actual landscape. In this particular
archaeological project, the major factor that decides the
settlement areas of prehistoric travellers is sustenance and
protection. The topology of the landscape has been
recovered in the previous phase together with the artefacts,
and a collective knowledge of vegetation types and their
preferences have been gathered which leads to a theoretical
reconstruction of the landscape, the missing step required to
complete the picture is to determine the vegetation formation
on the landscape. The strategies used in this phase focus
upon the ecological and biological behaviours of prehistoric
plants and the simulation and visualisation of their growth
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Plants reproduce in assigned seasons.
Seeds are dispersed in different directions and dispersal
agents are simulated by dispersing the seeds further.
Seeds have a period of dormancy before suitable
environmental conditions cause it to germinate; seeds expire if
they do not germinate within an assigned period of time.
Plant tolerance/adaptation to ecosystem factors is based on an
upper, ideal and lower value. Different plants have different
adaptations.
Plants compete for availability of sunlight, space and nutrients
based on environmental conditions and the sizes and shapes of
competing plants.
Global and local environmental conditions are defined
for the landscape:
Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds, CGames ’07 (Eugene Ch’ng)
Figure 3. Experimental Scenarios
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The global environment affectors – sunlight, temperature,
moisture, nutrients, elevation and carbon dioxide.
The local environment conditions affected by plants in close
proximity – effective sunlight, moisture and availability of
space.
Temperature-elevation ratio.
Hydrology.
Adaptation in vegetation denotes avoidance and tolerance to
environmental hazards. Adaptations of plants in botany
related studies have shown that over thousands of years,
survival of plants in extreme environmental conditions may
be countered via the development of physical characteristics
that are tolerant to hazardous conditions. The individual
fitness of plants is determined via the use of an adaptability
measure (Ch'ng 2007) developed in the research.
Evidence from experiments in section four with the
virtual plants using British prehistoric climatic settings
suggests strong evidence for supporting the application of
the artificial-life algorithms in the framework. Experiments
are thorough and include plant competition (space, nutrient,
sunlight), plant behaviours on temperature extremes
(altitudes, seasonal changes), soil types (texture, depth,
acidity), plant reproduction, seed germination, hydrology,
effects of sunlight and shade, and plant decay and its impact
on the growth of other plants. The study also observed
various emergent properties associated with natural
phenomenon observed in patterns of plant distribution such
as ecotones, ecoclines, climax community, and species
grouping. Figure 3 shows some screenshots from
experimental scenarios. During the research into artificial
life-based plants, it was realised that thousands of plant
interactions could result in resource bottleneck. An
optimisation algorithm (Figure 3d) was developed to
improve the efficiency of the interacting entities by
segmenting the landscape and associating each entity to its
interaction-group, thus greatly speeding up the simulation.
Visualisation in Games Engine
Visualisation uses CryTek’s CryEngine. CryEngine
were chosen because it is especially suitable for outdoor
landscape, it has many inbuilt functionalities for shaders and
outdoor rendering optimisations. Prior to that, various VR
toolkits and interactive 3D platforms were explored, namely
Worldviz’s Vizard, VR4MAX, and Shockwave 3D, but none
of the platforms come close to what a games engine can
offer in terms of life-like atmospheric effects and shader
capabilities. In the recent past, many archaeological
departments aiming to visualise their work were convinced
that only the high-end expensive graphics workstations such
as those afforded by SGI’s could meet their demands, using
the often lesser-than £20.00 games engine and the free editor
and SDK that comes with it has proven that games engines
has much more to offer not only in the often better rendering
capabilities, but also the ease of use, extensibility, and
multiplayer options available for cooperative archaeological
research in future applications. Games engines also boast a
much larger community support for developments as
compared to many VR toolkits.
The visualisation begins with the terrain import using
the Sandbox Editor. Trees generated in SeederEngine were
used to populate the CryEngine landscape and the habitat of
herbaceous species references the characteristic growth and
distribution of the artificial life framework. Based on the
settlement areas identified in the previous phase, the
Mesolithic houses and artefacts are grouped into villages and
bird flocks, fish and other insects were added for realism.
Figure 4 shows the screenshots.
Section four applies the simulation to selected regions of
the ancient river valley within an environment with
Mesolithic climatic settings, working with geoarchaeologists and palaeo-environmentalists to determine a
likely settlement area for hunter-gatherers based on their
diets and practicality – near river beds where clams and
hazel shrubs are in abundance.
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Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds, CGames ’07 (Eugene Ch’ng)
AKNOWLEDGEMENT
Special thanks to Prof Vince Gaffney, Director of the
Institute of Archaeology and Antiquity, The University of
Birmingham for his assistance in providing the original
seismic datasets of the Shotton River for the study. Thanks to
Dr Ben Gearey and Mr Simon Fitch of the same department
for their experience with the palaeo-environment, palaeobotany, Mesolithic cultures, and artefacts associated with the
virtual reconstruction. Thanks to Prof Robert Stone, who
managed the project and provided the necessary means for
its completion.
Figure 4. Visualisation in Games Engine
REFERENCES
CONCLUSION AND FUTURE WORK
The interdisciplinary research groups consisting of
computer scientists, geologists, archaeologists and palaeoenvironmentalists are finding the visualisation using games
engine very useful and are looking to expand the North Sea
visualisation work further to include extensions of the
landscape (Gaffney 2007). For example, researchers wishing
to ‘go back in time’ to the Mesolithic landscape could now
navigate and interact with the physics bound objects, access
hazel shrubs for nuts, sit by the crackling fire, scout the
banks for fish and clams, and to dwell in the 17 feet houses,
thus enabling them to re-live what was once impossible to do
so in early archaeology. Entertainment technology can now
provide better interpretation of archaeological sites via
virtual time-travel. The work has to-date generated popular
interests and has been featured in various international
media, websites, publications and TV programs.
New techniques developed as part of the second and
third research phases have potentials for application in
gaming environments. For example, the real-time plant
growth and distribution model could be extended and used in
environments that involve large passage of time and space
where the landscape or landmarks require changes, such as a
dynamic forest model or individual trees and plants as
landmarks in a Massively Multiplayer Online Game
(MMOG). Introducing evolvable landmarks into a gaming
environment could have an impact on the game play.
Similarly, the environmental and seasonal effects algorithms
can be employed by gaming environments to affect the
‘living’ entities for more realistic gaming experience (E.g., a
harsh winter could affect a virtual creature’s endurance and
vitality, causing it to be at a disadvantage during the season).
The optimisation algorithms for static entity-interaction
could also be used for more efficient agent-interaction in
multi-agent environments.
Future work will look into recreating virtual avatars
(Mesolithic man) that have artificial intelligence for
interaction with the users in a network environment.
Behavioural rules could be imparted into these virtual
avatars so that researchers could trace their routes of travel
and settlement patterns.
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BIOGRAPHY
Dr Eugene Ch’ng is currently a Senior Lecturer
at the School of Computing and Information
Technology, The University of Wolverhampton,
UK. He holds a PhD in Electronic, Electrical
and Computer Engineering at the University of
Birmingham and was a Research Fellow at the
same department. He has in the past worked on
a number of Virtual Reality and interactive 3D
projects related to the UK's defence arena as
well as various consultations in software,
multimedia and web application developments. His research
interests include Artificial Life, Autonomous and Multi-Agent
Systems, Virtual Environments, Augmented Reality, and Advanced
Interactive Systems.
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