Using Games Engines for Archaeological Visualisation - complexity.io

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


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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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:


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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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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Bayawa, M. (2006). "Digital Digs." Nature 440(27): 1106 - 1107. Binford, L. R. (1968). Debating Archaeology. New York, Academic Press. Ch'ng, E. (2007). "Modelling the Adaptability of Biological Systems." The Open Cybernetics and Systemics Journal 1: 13 - 20. Ch'ng, E., R. J. Stone, et al. (2004). The Shotton River and Mesolithic Dwellings: Recreating the Past from Geo-Seismic Data Sources. The 5th International Symposium on Virtual Reality, Archaeology and Cultural Heritage, VAST04: Interdisciplinarity or “The Best of Both Worlds”: The Grand Challenge for Cultural Heritage Informatics in the 21st Century. Ch'ng, E., R. J. Stone, et al. (2005). Evaluating Artificial Life-based Vegetation Dynamics in the Context of a Virtual Reality Representation of Ancient Landscapes. Virtual Systems and Multimedia. Ch'ng, E., R. J. Stone, et al. (2005). A Virtual Reality Archaeological Framework for the Investigation and Interpretation of Ancient Landscapes. Internet and Multimedia Systems and Applications, EuroIMSA 2005. Chapman, H. P. and B. R. Gearey (2000). "Palaeoecology and the perception of prehistoric landscapes: some comments on visual approaches to phenomenology." Antiquity 74: 316-319. Cremer J., S. J. (2001). "This Old Digital City" One Year Later: Experience Gained, Lessons Learned, and Future Plans. Proceedings of VSMM01:Enhanced Realities: Augmented and Unplugged. DeLeon V., B. R. (1998). Virtual Florida Everglades. VSMM98 Future Fusion: Application Realities for the Virtual Age. DeLeon V., B. R. (2000). "Bringing VR to the Desktop, Are you Game?" IEEE Multimedia 7(2). Ebert, D. and M. Singer. (2004). Retrieved 14 April. Ennis, G., Lindsay, M. (2000). "VRML Possibilities: The Evolution of the Glasgow Model." IEEE Multimedia 7(2). Fyfe, R. (2005). "GIS and the application of a model of pollen deposition and dispersal: a new approach to testing landscape hypotheses using the POLLANDCAL models." Elsevier: Journal of Archaeological Science xx(2005): 1-11. Gaffney, V. (2007). AHRC ICT Methods Network. Case Studies in Advanced ICT methods: North Sea Palaeolandscapes. United Kingdom, AHRC ICT Methods Network.

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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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