Novel Approaches to City Modeling: Generation and ... · different sides. In this paper we...

eCAADe 25 343-Session 08: City Modelling

Novel Approaches to City Modeling: Generation and Visualization of Dynamic Complex Urban Systems

Silke Berit LangCompetence Center for Digital Design & Modeling, ETH Zurichhttp://[email protected]

This year, for the first time in history more people are living in cities than in the country. This fact induced us to look at the topic of city modeling from different sides. In this paper we introduce novel approaches that contribute to the generation and visualization of dynamic complex urban systems. We distinguish reality-based and generic city models. On the one hand we look a three dimensional models of urban environments. On the other hand we are looking at the key challenges and trends that will shape future cities. We are drawing parallels to functional models of brain circuitry. City modeling as a case in point provides the basis for our research to arrive at a transdiciplinary theory of design and modeling.

Keywords: City modeling; generic modeling; reality-based modeling; mega- cities; sustainable cities.

Introduction

City modeling has evolved over the years and gains in importance not only in architecture and urban planning but also in economic development, securi-ty and defence, tourism and travel, game and movie industry, and entertainment. In our work the topic of city modeling is twofold: Large scale city model-ing refers both to generic, rather abstract models of mega-cities as well as the visualization of cities using 3D reality-based models.

In recent years, three-dimensional reconstruc-tion of our nature and man made environment is a rapidly growing application domain and is gaining increasingly importance. 3D models of urban envi-ronments are used in a broad field of applications such as urban planning, virtual reality, and navigation

systems as well as climate, air quality, fire propaga-tion, and public safety studies. Commercial users in-clude phone, gas, electric, communication, and real estate and tourism companies. Most of these users are primarily interested in models of buildings and other man-made structures such as terrain, vegeta-tion, and traffic networks. These are extended into facility management applications, where also the interior of buildings has to be modeled. A very spe-cial category of users is in the Cultural Heritage field where models of very high accuracy and resolution are usually required. Up to now, there is no city on the world which has an entire virtual 3D model. In addition, worldwide there is only a small minority of city models at an acceptable level of detail and extension. Ideally, reality-based 3D city models com-bine relevant elements of a particular spatially- and

344 eCAADe 25 - Session 08: City Modelling

application-wise defined region in computer com-patible form. These models include terrain models and buildings as well as trees and vegetation, traffic and utility lines etc. Computer technology, Computer Graphics, Computer Aided Design (CAD), Spatial In-formation System (SIS) technology, research in pho-togrammetry and computer vision as well as sensor technology and robotics now offer powerful tools for collecting, storing, analysing, and visualizing digital data for city models. This makes it today possible to do 3D city modeling research on a large scale.

According to the future prospects from the Unit-ed Nation which was presented by Kofi Annan end of January 2005 in New York, half of the earth popula-tion will be living in cities within the next two years. Also more than twice as much of the amount of to-day’s elderly people will live in measurable time on our earth. Accordingly, in 2055 the amount of peo-ple over 60 years will be trebled to more than 2 bil-lions. The migration into cities increases and today’s metropoles are continuing growing. Today’s largest cities are Tokyo (35,3 million residents), Mexico- City (19,2 million residents), New York (18,5 million resi-dents), and Mumbay (18,3 million residents). In the future we will have mainly three types of mega-cities:

Emerging cities which are characterized by high •growth rates driven by migration and natural growth (Cairo, New Dehli, Lagos).Transitional cities which are affected by dynamic •growth and mostly in countries that are more than 50% urbanized (Istanbul, Moscow, São Paulo).Mature cities with much slower growth rates •with a stagnating and aging population (Lon-don, New York, Paris).

For the most part vast growth of population as well as flows of migration bring out serious challenges for a positive economical, environmental, and social de-velopment. Consequentially we have to find meth-ods of resolution for the society of tomorrow living in mega cities which are able to manage this new form of cityscape ecological, sociopolitical, and economi-cal. To face this challenge capable and innovative

infrastructure solutions as well as new approaches to metropolitan governance have to be developed. Al-ready in 1951 Isaac Asimov described in his original Foundation trilogy the vision of an urban gigantism. He predicted the end of the fictive planet Trantor because of mega urbanization. In order to prevent that reality will catch up with us and Asimov’s vision will no longer just an utopian construct of ideas we have to work on strategies for achieving long-term ecologically balanced urban settlements.

Background

Google Earth (http://earth.google.com/), Micro-soft Virtual Earth (http://www.microsoft.com/vir-tualearth/default.mspx) and NASA Worldwind are going to provide on the cityscape 3D real-time vi-sualization solutions. 3D Reality Maps (http://www.realitymaps.de/) already offers fascinating 3D photo-realistic city maps from Potsdam, Munich, and Berlin. Since this is an innovation of the RSS Remote Sens-ing Solution GmbH they use the aerial views and height model of the High Resolution Stereo Camera (HRSC) developed at the German Aerospace Center (DLR). The Swiss/ American CyberCity Group (http://www.cybercity.tv) maintains a constantly growing database of 3D city models including Los Angeles, Chicago, Paris, Hamburg as well as Beijing and Hong Kong.

Using data from photorammetry or airborne laser scanners in combination with 3D digital Ter-rain Models (DTM) it is possible to generate 3D city models relatively cost-efficiently. Textured with high resolution orthophotos users can freely navigate in virtual 3D scenes. 3D models and 3D landmarks are mainly used in the following applications: Urban planning is one of the most important application fields. Especially in the decision making process like reconstruction projects of old town areas, invest-ment projects or new road constructions govern-ments can barely imagine out of 2D drawings, writ-ten text and tables with numerical information how the civil works influence their city. 3D city models in


combination with DTM also serve as a basis in disas-ter control. For example, flood simulations visualize which part of a city will be affected as well as the im-pact on buildings. An engineering application could be the maintenance and simulation of a city sewer system. Structures, connecting sewers, street names, proposed construction sites, 3D buildings are includ-ed in geo-referenced 3D computer environment. Also for the simulation of traffic noise from several sources and contaminants in the air of large cities requires detailed 3D city models. Photorealistic 3D models are also used by real estate companies and the government in marketing processes to acquire and recruit investors and vendees. 3D city models are on the one hand used by tourists who are plan-ning their holidays. On the other hand the hotel and restaurant industry use them to present their prod-uct and their services. Further application fields are homeland security and navigation systems. Not to forget the entertainment industry with their applica-tions in TV, movies, and computer games.

Applications in urban planning, disaster control and engineering have the potential for mastering the emerging spatial problems of emerging mega-cities worldwide, especially in developing countries. Cities like Shanghai, Beijing, and Chongqing are rapidly growing. Prognoses state that 90 percent of global population growth will be in cities between now and 2030. Therefore, infrastructures and the environ-ment have to be adapted to the changing demands and new urban development strategies have to be developed. The big challenge is the development of dynamic city models that are able to adapt to the rapidly growing and changing cities.

Generic modeling

The rapid growth of cities like Shanghai, Jakarta, Mumbai, and Mexico City, so called mega-cities, is a big challenge for urban planning, urban geography, urban economics, urban management, landscape design, regional and infrastructure planning, envi-ronmental sciences and other related disciplines. For

the very first time in the middle of 2007 more people will live in cities than on the country side. Until 2015 the number of cities with a population in excess of 10 million people will grow from 300 up to 560 so that 350 million people will live in mega-cities. One main reason for this above average growth lies in the economic attractiveness of metropolitan regions. Even though a lot of research is done within these fields, the big picture of generic mechanisms of the underlying spatial dynamics of urban growth is still missing. Although much modeling work has been undertaken in the past, the mechanisms govern-ing the evolution of cities are far from being well understood. The growth of mega-cities especially the formation of urban structures like settlements, trail systems, transport, and supply networks can be compared on a structural perspective to the devel-opment of self-organized structures known in phys-ics. Mega-cities might evolve towards a hierarchical form of spatial organization. Urban modeling there-fore can be set into relation to the modeling of com-plex systems where new qualities emerge due to the dynamic interaction of sub-entities. The growing urbanization as well as the demographic change in-crease massively the need of powerful and efficient infrastructures which are determined by cultural, sociological, economic, political, and ecological fac-tors. The cities of tomorrow have to be environmen-tally, socially, and economically sustainable. This calls in particular for modeling complex systems such as the dynamics of urban evolution and the design of ecologically balanced urban regions.

Although each city has its own unique issues and circumstances to address, they all face hugely complex social and environmental challenges. One challenge will be to find innovative solutions to constantly changing boundary conditions. Infra-structure solutions as well as sustainable allocation must work simultaneously at various scales: form territorial planning to the very small scale of build-ing construction. Therefore, the challenge is to in-vestigate a mechanism which is able to adapt to the rapidly changing environment. In fact, adaptation is


one of the key features in nature as well as one of the foundational principles of evolution. Living organ-isms are able to constantly improve their efficiency under changing conditions. The development of agent-based systems enables the generation of sample solutions. These agents must have the ability to constantly gather information about the environ-ment and in addition they have to develop strategies which match the different environments.

Latest research of Frank Schweitzer, Professor for Systems Design at ETH Zurich, is the investigation of applications of complex system theory on dynamic organisations. Key issues are in addition to quanti-tative modeling and computer simulations the de-velopment of formal concepts. The focus thereby is on social and economical interaction. This research which leads back to the approach of self-organiza-tion is applied to the field of urban structure forma-tion (see figure 1). The expansion of physical laws and principles on social and economical questions will help make a contribution to master the key challeng-es of dense, vast, and complex mega cities. Bringing in methods and knowledge from physics into the analysis and modeling of social-economical process-es enables gaining novel insights into the dynamics of these systems (Schweitzer, 1997). This transfer of methods is especially helpful, for example, in mod-eling expected growth of cities, flows of traffic and congestions, in simulating pollution or in collective decision making within social systems. This kind of transfer of methods is aligned with an abstraction of the problem, especially in modeling the interaction of actors. Although certain reality aspects are dis-regarded longtime research in the field of complex systems proves that individual characteristics often have a secondary influence on the overall dynamic (Schweitzer, 2003). Physically-based models can be successfully applied to forecast human behaviour in panic situations, in pedestrian areas or in using es-cape routes. This research makes a contribution to project and anticipate future developments of ur-ban systems taking into account varying constraints as well as changing parameters.

Reality-based modeling

Up to now there are no standards available for 3D city modeling. This section describes novel methods for creating reality-based models which often com-bine geometry and texture investigated by mem-bers of the Competence Center for Digital Design & Modeling at ETH Zurich. The focus thereby is the reconstruction of existing cities as well as destroyed ancient sites. The process of 3D city modeling is dived up into data acquisition and reconstruction.

Data acquisitionToday, the data is acquired by using different meth-ods based on pure camera images (photogram-metry, computer vision) or by the use of multi-sensor sensor fusion methods. The need to generate large and complex city models, the problems of maintain-ing (updating) these models, the complexity of ap-plication demands, and technology challenges still make large scale city modeling an intensive research area. This calls in particular for further automation of the data acquisition procedures and for more ef-ficient techniques for data handling, in particular interactive visualization. The existing methods for generating the immense data needed are still very rudimentary and relatively slow. Basically two tech-niques are established: (1) derivation from aerial and terrestrial laserscans, (2) derivation from aerial and/ or satellite images.

Figure 1Simulation of the rank-size cluster distribution of Daegu from 1988 to 2010 -grey: simulated growth area (Credit ETH Zurich, Chair of Systems Design)


Using the multi-sensor sensor method which is developed at the Autonomous Systems Lab at the ETH Zurich (Professor Roland Siegwart) the map-ping is done with the information of a 3D range laser and perspective/omnidirectional camera images. Hereby the sensors are put onto a moving platform, a Daimler Chrysler smart vehicle. Structure as well as dynamic appearance information is continuous retrieved. Therefore, a point cloud can be retrieved online, matched, corrected, and colored in real time. The result is a scene modeled online but without any contextual information inside. The challenge in future research is to find a method which allows ex-tracting semantic information and adding it to the retrieved 3D point clouds.

ReconstructionIt is possible to produce a 3D model only based on photographs. But these models are on the one hand noisy and on the other hand most of them lack of realism. In addition these models are still relatively large in size. That’s way the Computer Vi-sion Lab at ETH Zurich (Professor Luc Van Gool) de-velops new techniques to extract 3D shapes of ob-jects and scenes from video and image data. They work on the 3D modeling of scenes from simple images or video sequences using cognitive loops (Cornelis, 2006a) as well as on shape grammars for procedural modeling which exploit the regular-ity presented in many buildings (Mueller, 2007). They developed a system which is able to create an automatic 3D city modeling from video input streams (see figure 2). In doing so they use a real time 3D city modeling algorithm which uses the assumption that building facades and roads can be modeled by simple ruled surfaces (Cornelis, 2006b). Grammar guided modeling is used to pro-duce a model of a building based on images (see figure 3). Applying set of rules which exploits the regularity presented in many buildings and the semantic labeling of same elements like windows and doors have a dramatic effect on the visual quality. These rules are also used to clean up the

data and fill in missing parts coming from images which contain occlusions like trees, cars or people (Mueller, 2007).

The Group of Photogrammetry and Remote Sensing at ETH Zurich (Professor Armin Grün) devel-oped semi-automated procedures for value-added data generation from images. Their approach fol-lows the following components. First, they generate a fully automated digital surface model of the area of interest using aerial images. The data is acquired, for example, using helicopters or robots equipped with cameras. Than they manually identify features of in-terest. Image matching for the definition of a feature in object space is done using their Least Squares Images Matcher. After that the 3D model is created from the generated point cloud. The result is a 3D geometric model including roofs and terrain but no facades. The facades are mapped on the model us-ing textures (see figure 4).

Figure 2Real- world 3D city model using recorded video data as input and cognitive loop (Credit KU Leuven and ETH Zurich, Computer Vision Lab)

Figure 3Image-based procedural modeling of facades from images of arbitrary resolu-tions (Credit ETH Zurich, Computer Vision Lab)


Combination of methodsWe can notice that each method has its restrictions and disadvantages. Using the multi sensor senor method contextual information is missing and methods to extract semantic information to the 3D point cloud need to be investigated. This contex-tual knowledge can be added to a scene by defin-ing semantic rules, semantic object classification and 3D model simplification. The semi-automated procedure developed by the Group of Photogram-metry and Remote Sensing also provides a 3D model without facades. Therefore, we are combining the described approaches in a next step. Knowledge in shape grammars as well as contextual knowledge of scene for façade reconstruction form the Computer Vision Group will be combined with the knowledge of developing robots which are able to understand, interpret, and represent the environment done by the Autonomous Systems Lab. These robots are used to acquire aerial data for needed for the pho-togrammetry approach. We believe that we are with this transdisciplinary approach able to develop and investigate novel approaches for high quality reality-based 3D city models.

Computational neuroscience

Although the inference of functional models of brain circuitry distinguishes itself from 3D models of ur-ban environments by very different constraints we believe that understanding how the brain works would offer an advance in the design of cities. In or-der to unravel brain functions and to decipher the dynamics of neuronal circuitry effective and func-tional models are built. Analog to city modeling the challenge thereby is how much detail is necessary for characterizing the functional behavior. Similar to reality-based city modeling functional models of brain circuitry are extracted from sparse data sourc-es. Being able to adapt the functionality of the brain we have to understand its remarkable uniform archi-tecture first. In order to be able to explain brain oper-ation the understanding of self-construction, self-re-pair and self-programming would be helpful. Using a combination of generic and learned information the neocortex is able to construct and configure itself. Neuronal networks set up sensory and motor con-tact with their surrounding environment. Within the construction process these networks configure and tune themselves for generating effective and intelli-gent behavior for the organism (Douglas, 2006). The Institute of Neuroinformatics at ETH Zurich already has shown how a complete simple behaving organ-ism can construct itself from a single precursor cell, using plausibly biological mechanisms (Roth, 2006). These principles will now be applied to assemble the layered neuronal structure of cortex. However, these principles could be applied also for the design, con-struction, and operation of artificial systems such as territorial planning on the large scale and buildings on the small scale.

Our approach

We can observe that we have failed to merge histori-cal insights (e.g. Alberti’s books or Christopher Alex-ander’s patterns) and novel computer techniques in architecture for designing future cities so far.

Figure 4Auto-textured 3D city model with detailed roof structured derived from stereo aerial images (Credit ETH Zurich, Group of Photogrammetry and Remote Sensing)


Furthermore, methodologies from other disciplines such as photogrammetry based city reconstruc-tion, insights on landscape planning, insights from self-constructing neuronal systems, abstraction of cellular mechanisms, and collaboration between dis-tributed agents are not linked to the process of large scale city modeling. Man-made systems like cities become more complex and autonomous. Therefore, we need novel methods that offer self-construction, adaptation to changing conditions as well as self-repair.

As the topic of city modeling is manifold, a topi-cal research field in different disciplines and requires design as well as modeling knowledge we use this re-search field as a case in point to investigate a design and modeling terminology across disciplines. Our research is also driven by the observation that there does not exist a systematic, integrated, and funda-mental understanding of the various aspects of de-sign and modeling. Therefore, we are looking in our research at large scale city modeling as a whole (see figure 5), including generic and reality-based model-ing as well as self-constructive design, and city and building design. The novelty of our research is that we are drawing a parallel between large scale city modeling and object recognition on the one hand and understanding the 3D neuronal structures in the brain and the underlying self-constructing design on the other hand. Abstracting from particular prob-lems in neuroinformatics such as neuronal circuit reconstruction, amount of cells and their classes in a brain, helps us to understand the functions and re-quirements for dynamic large scale city models. With the help of architectural methods we will extract the methodologies from neuroinformatics and from computer science and apply them to city modeling. The architect’s ability to visually reduce complex in-formation is applied to represent the living cell with the goal to better understand its complexity.

To be able to achieve this goal we address neu-ronal structures and cities as two case studies, one from the side of generic and living systems, and the other from the side of reality-based systems.

Both have the mix of different scales in common. For example, details about synaptic connections in neuronal structures and single buildings in cities. On the larger scale we deal with configurations contain-ing millions of neurons or thousand of houses. To be able to extract a design and modeling terminology across disciplines from our research we have two layers: modeling and design. Under modeling we gather knowledge about the static (the anatomical aspects of cells and their layout, the buildings, public spaces, and surrounding landscape of cities) as well as dynamic (adaptation and plasticity in the case of the brain, and traffic, noise levels etc. in the case of cities) domains adaptation, 3D neuronal structures, behavior in cities and 3D city modeling. This knowl-edge is used to get insights into the underlying self-constructive design on the one hand and building and city design on the other hand. Providing a feed-back loop between the layers the results will be veri-fied and improved.

Summary and conclusions

Cities are extremely complex systems and unique par excellence. The challenges we are facing today in urban planning is forecasting and modeling the

Figure 5Large scale city modeling as a case in point to find a de-sign and modeling terminol-ogy across disciplines


dynamics of mega-cities and growth-limited cities as well as considering the wide range of influencing parameters and forces. We are looking at the chal-lenges from a transdisciplinary point of view and are bringing in our research experts from several disciplines. We believe that in order to face the chal-lenges of future cities research must be grounded in transdisciplinary work. Therefore, one of our ma-jor research clusters within the Competence Center for Digital Design & Modeling is dedicated to Large Scale City Molding. The focus on Large Scale City Modeling as a whole will force the researchers to extract commonalities between the different prob-lem scenarios and abstract from application specific detail for design and modeling. We believe that we are with this approach on the one hand able to con-tribute in providing new dynamic city models and on the other hand to discover design and modeling principles which will form the core of a design and modeling science. After almost two years of prepara-tory work and analysis carried out within our Com-petence Center for Digital Design & Modeling, we firmly believe that the best possible way to imple-ment a transdisciplinary research infrastructure for design and modeling is doing a joint research proj-ect at the graduate level. This arises from the insight that excellent scientific achievements are performed by young researchers who are able to develop their own projects within an innovative educational and research context. However, there is still the not to underestimated fact that young researchers have to learn to work in transdisciplinary teams without los-ing the solidarity and thoroughness which is still the excellence of disciplinary work.

Acknowledgements

We would like to thank all members of the Compe-tence Center for Digital Design & Modeling for their inspiring discussions about city modeling. This work was supported and stimulated with fruitful discus-sions by Professor Armin Grün, Professor Luc Van Gool, Professor Frank Schweitzer, Professor Roland

Siegwart, and Professor Rodney Douglas. Special thanks go to Professor Gerhard Schmitt, Professor Markus Gross, Professor Joachim Buhmann, and Remo Burkhard for creative discussions and valuable comments.

References

Cornelis, N., Cornelis, K., Van Gool, L.: 2006b: “Fast Com-pact City Modeling for Navigation Pre-Visualization”, In IEEE International Conference on Computer Vi-sion and Pattern Recognition (CVPR’06), New York.

Cornelis, N., Leibe, B., Cornelis, K., Van Gool, L.: 2006a, “3D City Modeling Using Cognitive Loops”, 3rd In-ternational Symposium on 3D Data Processing, Vi-sualization, and Transmission (3DPVT’06), Chapel Hill, USA.

Douglas, R.: 2006, “Expressing feasible structure: Explor-ing principles for the self-construction of neural networks, and simple behaving organisms”, DDM Colloquium ETH Zurich, Zurich, Switzerland, 15. June 2006.

Mueller, P., Zeng, G., Wonka, P., Van Gool, L.: 2007, “Im-age-based Procedural Modeling of Facades”, Pro-ceedings of ACM SIGGRAPH 2007 / ACM Transac-tions on Graphics (to appear).

Roth, F., Siegelmann, H. and Douglas, R.: 2006 “The self-construction and -repair of a foraging organism by development from a single cell”, Artificial Life, in press.

Schweitzer, Frank: 2003, “Meinungsbildung, Kommu-nikation und Kooperation aus physikalischer Pers-pektive”, in: Physik Journal, Deutsche physikalische Gesellschaft, pp. 57-62.

Schweitzer, Frank; Steinbrink, J.: 1997, “Urban Cluster Growth: Analysis and Computer Simulation of Ur-ban Aggregations”, in: Self-Organization of Complex Structures: From Individual to Collective Dynamics (Ed. Frank Schweitzer), Gordon and Breach, London, pp. 501-518.

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