6. Urban models are important! Entertainment Why should you learn about it?
7. Urban models are important! Entertainment Mapping and visualization Why should you learn about it?
8. Urban models are important! Entertainment Mapping and visualization Urban planning Why should you learn about it? time
9. Solving the content problem As computing and display capabilities continually improve, audience expects ever higher quality digital content Traditional tools are insufficient for increasing demand and few tools are available for efficient large-scale urban modeling What is the main challenge?
10. Geometric content creation: Procedural Methods: tools to “program” the geometry of buildings, parcels, roads, facades… What are the current approaches?
11. Geometric content creation: Procedural Methods: tools to “program” the geometry of buildings, parcels, roads, facades… Non-geometric content creation: Urban Simulation Methods: algorithms to “simulate” urban environments (e.g., social, economic, and some geometric aspects) What are the current approaches?
12. Outline Introduction Modeling of Urban Spaces Geometric and Behavioral Urban Modeling Conclusions, Challenges, Open Problems Questions
13. Modeling of Urban Spaces The urban modeling pipeline Buildings Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views
41. Procedural Modeling of Cities Procedural Modeling of CitiesParish and Müller SIGGRAPH 2001
42. Procedural Modeling of Cities Input: Various image maps Terrain elevation Population density Output: Urban Model System of highways and streets Blocks and lots Building geometry
43. Procedural Modeling of Cities Approach Road network: Extended L-systems considering global goals and local constraints Global: Street patterns and population density Local: Land/Water/Park boundaries, elevation, crossing of streets
44. Procedural Modeling of Cities L-systems Generation of plantsPrusinkiewicz, Lindenmayer; 1990 Environment-sensitivePrusinkiewicz, James, Mech; 1994 Interaction (Open L-System)Mech, Prusinkiewicz; 1996 EcosystemsDeussen, et al.; 1998
45. Modeling of Urban Spaces Topics to be covered in more detail: Procedural Modeling of Cities(Seminal paper by Parish and Müller, 2001) Modeling of buildings (3D structures) Modeling of urban layouts (2D structures) Buildings Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views
46. Modeling of Buildings Facades Instant Architecture Image-based Procedural Modeling of Facades (semi-automatic rules generation) Buildings Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views
48. Modeling of Facades Input: Target building design Output: Textured 3D models of building facades
49. Modeling of Facades Approach: Split grammars Used instead of L-systems L-systems simulate growth in open spaces (better for plants and road networks) Buildings have stricter spatial constraints and their structure does not reflect a growth process
57. Modeling of Buildings Mass Procedural Modeling of Buildings Interactive Visual Editing of Grammars for Procedural Architecture (semi-automatic rules generation) Buildings Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views
58. Modeling of Building Mass Procedural Modeling of BuildingsMüller, Wonka, Haegler, Ulmer, Van Gool SIGGRAPH 2006
59. CGA shape production process: Iteratively evolve a design by creating more and more details Sequential application (like Chomsky grammars) Starting shape (axiom) is a box Modeling of Building Mass
66. Shape interaction problem: The volumes are notaware of each other Unwanted intersectionsare generated Modeling of Building Mass
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70. Modeling of Buildings Simultaneous Mass and Facades Style Grammars for Interactive Visualization of Architecture Continuous Model Synthesis Buildings Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views
71. Modeling of Mass with Facades Style Grammars for Interactive Visualization of ArchitectureAliaga, Rosen, Bekins TVCG 2007
72. Modeling of Mass with Facades Approach (inverse modeling): Infer a grammar for creating architecture and buildings Enable rapid generation of a building in the style of others
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75. Modeling of Mass with Facades Example result Building Photographs (parse) (derive)
76. Modeling of Mass with Facades Example result Photograph Novel building Novel building plus landscaping
77. Modeling of Mass with Facades Example result Photograph Model editing In-place viewing
78. Modeling of Mass with Facades Procedural buildings (from aerial photographs) Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
79. Modeling of Mass with Facades Procedural buildings (from aerial photographs) Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
80. Modeling of Mass with Facades Procedural buildings (from aerial photographs) Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
81. Modeling of Mass with Facades Procedural buildings (from aerial photographs) Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
82. Vanegas, C. A., Aliaga, D. G., and Beneš, B, “Building Reconstruction using Manhattan-World Grammars”, IEEE Computer Vision and Pattern Recognition, (CVPR), 2010.
83. Modeling of Urban Spaces Topics to be covered in more detail: Procedural Modeling of Cities(Seminal paper by Parish and Müller, 2001) Modeling of buildings (3D structures) Modeling of urban layouts (2D structures) Buildings Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views
84. Modeling of Urban Layouts Example-based Urban Layout SynthesisAliaga, Vanegas, Benes SIGGRAPH Asia 2008
85. Modeling of Urban Layouts Input: Example urban layout Images (aerial view)+ Structure (streets, parcels)
86. Modeling of Urban Layouts Input: Example urban layout Output: New synthesized urban layout that looks like the example layout
87. Modeling of Urban Layouts Observation: Both image and structure information about the urban layout available Courtesy of Google Maps Image: aerial view Structure: street + parcels
88. Modeling of Urban Layouts Approach: Simultaneously synthesize structure and image Image: aerial view Structure: street + parcels
96. Modeling of Urban Layouts Interactive Reconfiguration of Urban LayoutsAliaga, Benes, Vanegas, Andrysco IEEE CG&A 2008
97. Modeling of Urban Layouts An editor providing tools to expand, scale, replace and move parcels and blocks of existing layouts Exploits connectivity and zoning of parcels
98. Modeling of Urban Layouts Uses a solver to find a planar transformation for each tile that best accommodates the changes caused by the editing operations Two types of error: Gap error + Deformation error
99. Modeling of Urban Layouts Procedural Modeling of StreetsChen, Esch, Wonka, Müller, Zhang SIGGRAPH 2008
101. Modeling of Urban Layouts Tensor fields Second order symmetric tensor fields Eigenvectors of tensorvalues for twoorthogonal families
102. Modeling of Urban Layouts Tensor fields Second order symmetric tensor fields Eigenvectors of tensorvalues for twoorthogonal families Topology Singularities Hyperstreamlines
103.
104. Geometric and BehavioralUrban Modeling Carlos VanegasAdvisor: Daniel AliagaCollaborators: Bedrich Benes, Paul WaddellDepartment of Computer SciencePurdue UniversityCollege of Environmental Design,UC Berkeley
105. Motivation Urban spaces (e.g., districts, towns, cities) are a collection of man-made structures arranged into parcels, blocks, streets, and neighborhoods
106. Motivation But, the structures of an urban space are the scenarios where behavioralprocesses take place Thus, these structures are influenced by the behavioral processes
107. Geometric Modeling Geometric modeling of urban spaces has become a popular research area in Computer Graphics Several works have been presented to address different parts of the urban modeling pipeline Buildings, Landscapes Roads Mass Facades Major Roads Minor Roads Blocks Lots Input Aerial Views 2D 3D
108. Behavioral Modeling Behavioral modeling of urban spaces is studied in several disciplines (e.g., urban planning, earth and atmospheric sciences, civil engineering, etc.) Goals: Understanding the underlying socio-economic and environmental processes occurring within an urban space Assisting decision-making of urban policies in current and future urban spaces
109. Behavioral Modeling A Generalized Framework* (*) Wegener, M. “Operational urban models state of the art”. Journal of the American Planning Association 60, 17–29, 1994
110. Geometric + Behavioral Modeling Two research areas separately study two types of properties of one common space
111. Geometric + Behavioral Modeling Two research areas separately study two types of properties of one common space Buildings, Landscapes Roads Mass Facades Major Roads Minor Roads Blocks Lots User Aerial Views Geometric Modeling 2D 3D
112. Geometric + Behavioral Modeling Two research areas separately study two types of properties of one common space Geometric Modeling 2D (Roads, Parcels) 3D (Buildings, Landscape)
113. Geometric + Behavioral Modeling Two research areas separately study two types of properties of one common space Behavioral Modeling Geometric Modeling 2D (Roads, Parcels) 3D (Buildings, Landscape)
116. Geometric + Behavioral Modeling Isolation results in: Functional disconnection between results Models that do not necessarily resemble real-world spaces Simulations that do not consider changes and specific layouts in the geometry Behavioral Modeling Socio-Econ Simulation Weather Simulation Traffic/Crowd Simulation User Geometric Modeling 2D (Roads, Parcels) 3D (Buildings, Landscape)
117. Geometric + Behavioral Modeling Integration brings advantages to a number of applications Behavioral Modeling Socio-Econ Simulation Weather Simulation Traffic/Crowd Simulation User Geometric Modeling 2D (Roads, Parcels) 3D (Buildings, Landscape)
118. Geometric + Behavioral Modeling Integration brings advantages to a number of applications Applications Behavioral Modeling Urban Planning and Design User Geometric Modeling Socio-Econ Simulation 2D (Roads, Parcels) Content Generation Weather Simulation Emergency Management Traffic/Crowd Simulation 3D (Buildings, Landscape)
119. Example Applications Urban Visualization Infer an urban layout (images + structure) from the values of a set of (precomputed) simulation variables at any given time step Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery”, IEEE TVCG, 2009.
120. Example Applications Urban Visualization Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery”, IEEE TVCG, 2009.
121. Example Applications Urban Visualization Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery”, IEEE TVCG, 2009.
122. Example Applications Urban Planning Analysis of urban development scenarios(in collaboration with Paul Waddell, from UC Berkeley) Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
123. Example Applications Urban Planning Height of buildings in downtown increases Number of jobs increases Population increases New housing appears in accessible areas Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
124. Example Applications Urban Planning Town center Population Parcels Parks Jobs Buildings Terrain Input Output Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
125. Example Applications Content Generation Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph. 28 (Proceedings SIGGRAPH Asia), 2009.
126. Example Applications Content Generation Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph. 28 (Proceedings SIGGRAPH Asia), 2009.
127. Example Applications Content Generation 225 Km2 Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph. 28 (Proceedings SIGGRAPH Asia), 2009.
128. Geometric Modeling Procedural road generation (from behavioral data) Generate set of seeds based on population/jobs distribution Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
129. Geometric Modeling Procedural road generation (from behavioral data) Each seed is used as an intersection of the arterial roads network and used to generate arterial segments Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
130. Geometric Modeling Procedural road generation (from behavioral data) Street seeds are generated along arterial road segments and used to create streets Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
132. Geometric Modeling Procedural blocks and parcels generation Voronoi-diagram based methods Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
133. Geometric Modeling Procedural buildings (from behavioral data) Vanegas, C. A., Aliaga, D. G., Beneš, B., and Waddell, P, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Trans. Graph.28 (Proceedings SIGGRAPH Asia), 2009.
134. Current and Future Work Validation of urban simulation model Equilibrium Equilibrium
135. Current and Future Work Validation of urban simulation model Real City Synthetic City
136. Weather Simulation Can cities be designed aiming to minimize the occurrence of undesirable meteorological phenomena? (in collaboration with Purdue EAS) Possible approach requires: Fast, interactive editing of a 3D urban model Automatic generation of urban morphology data from urban model (desirable) Closed-loop simulation automatically adjusts the 3D model to reach a set of given meteorological results
138. How to access these technologies? Industry: CityEngine (www.procedural.com) Developedby Pascal Müller andProcedural Inc., Zürich
139. Outline Introduction Modeling of Urban Spaces Geometric and Behavioral Urban Modeling Conclusions, Challenges, Open Problems Questions
140. Conclusions Summary of topics Modeling of Urban Spaces (pipeline and methods) Geometric and behavioralurbanmodeling
141. Conclusions Generating realistic and plausible models of urban spaces is a great challenge Fast, accurate modeling of urban spaces is of significant interest to several applications
142. Challenges On one hand… Urban modeling methods aim to make more efficient the 3D design of urban spaces On the other hand… Urban simulation models get better at representing the complex processes occurring in urban spaces
143. Challenges Multidisciplinary goal: Take advantage of urban simulation models to device more efficient and intuitive methods for generating realistic 3D urban models Use content generation methods to facilitate the visualization of the results generated by urban simulation
144. Conclusions Tight relation between Academia and Industry in Computer Graphics (mostly thanks to SIGGRAPH!) Gettingtoknow SIGGRAPH papers is a way to see the “coming attractions” of Computer Graphics software We have some CG research in Colombia. There’s a need to make it more visible (have them publish at SIGGRAPH) and link it to the animation industry
145. Outline Introduction Modeling of Urban Spaces Geometric and Behavioral Urban Modeling Conclusions, Challenges, Open Problems Questions
146. Questions Thank you! Acknowledgements Daniel Aliaga, Bedrich Benes, Jie Shan, Purdue University Remco Chang, University of North Carolina, Charlotte Guoning Chen, Gregory Esch, Oregon State University Bernard Frisher, IATH Simon Haegler, Pascal Müller, Basil Weber, ETH Zürich Aaron Hertzmann, University of Toronto Markus Lipp, TU Wien Paul Merrell, University of North Carolina Chapel Hill Procedural Inc. (CityEngine) Peter Wonka, Arizona StateUniversity Paul Waddell, UC Berkeley
Hinweis der Redaktion
Thank you all for attending this talk.
Urban modeling is the process of creating digital models of real or virtual cities.This process is actually quite challenging because a city is a complex collection of architectural structures. These structures are arranged into buildings, parcels, blocks, neighborhoods and roads.
- entertainment - fast generation of detailed digital content for populating urban areas in video games and movies,
- mapping and visualization - reconstructing existing urban spaces for mapping and navigation tools, visualizing previously-existing cities for which only partial data exists, and allowing architects to visualize a new city,
- urban planning - predicting outcomes of land use policies and their effect on existing neighborhoods, and creating hypothetical views of an urban space after applying development and growth algorithms
In film, game and other applications, consumers expect richer and higher quality digital content for their moneyContent producers cannot address this problem by just increasing the time of modeling using traditional tools… tools that allow the automatic or semiautomatic generation of urban models are necessary.
A solution to the content problem is Procedural Content Generation.It consists of encoding the structural, spatial and functional complexity of cities and buildings… and use that encoding for the efficient creation of detailed building models at low cost
A solution to the content problem is Procedural Content Generation.It consists of encoding the structural, spatial and functional complexity of cities and buildings… and use that encoding for the efficient creation of detailed building models at low cost
The existing methods for procedural urban modeling roughly use this pipeline or a part of it
There are different types of input to geometric urban modeling methods, including:…
Several approaches have been presented for procedural road generation, including:…
[15 MINUTES]
We’ll start by an overview of the seminal paper by Parish and Müller, 2001, and give a brief introduction to L-systems and shape grammars which are used in this work
Just as image based façade modeling made editing of shape grammars for facades easier, this work simplifies the process of creating shape grammars for procedural modeling of buildings
In a second method, 3D textured models of buildings are generated from oblique-angle aerial photographs and the calibrated geometry of the building footprint.The general idea is to assume that the model is the 3D bounding box of the building, and then move vertically along the building from bottom to top.At each floor, we modify the building contour to optimize the matching between the estimated 3D model and the photometric data.
As a preprocessing step, the aerial photographs are segmented and the background removed.
The contours at each floor are adapted to the photometric data
Projective texture mapping is then performed using the input photos
These are some results that we have obtained.Notice that image-based modeling of buildings is an alternative to procedural modeling for fast generation of 3D buildings to populate large urban spaces.
Say that this work is inspired in texture synthesis and show some images on texture synthesis by Hertzmann et al.
Vector data of streets is widely available Vector data of parcels can be obtainedsecuencial
Thank you all for attending this talk.
Urban spaces are collections of man-made structures arranged into parcels, blocks, streets, and neighborhoods.As illustrated in these two photos of Tokyo, these structures are distributed throughout the terrain in complex patterns, and each structure by itself frequently has detailed geometry.
Now, the physical structures of a city are just the scenario where several complex behavioral processes occur.And the appearance of the city itself is ultimately influenced by such processes,as well as the behavioral processes are influenced by the appearance of the city.
Due to its applicability and complexity, urban modeling has become a popular research area in computer graphics during the last years, and we have seen a proliferation of urban modeling papers in the main computer graphics conferences.Several works have been presented to address different parts of the urban modeling pipeline, including 2D and 3D geometry.We’ll discuss some of them later.
Several disciplines study how to model the behavior of cities. These disciplines include urban planning, earth and atmospheric sciences, and civil engineering, among others.Disciplines studying behavioral modeling do not deal directly with the structures of the city, but rather aim to understand the socioeconomic and environmental processes that take place within an urban space, and toHelp decision makers to establish policies in current and projected urban spaces.
In a similar way to how urban geometric modeling is summarized by the modeling pipeline that I just showed,Urban behavioral modeling is described by a generalized framework.This framework shows the relationships that urban simulation models attempt to represent in varying degrees of comprehensiveness.
So, up to this point, I have introduced two research areas that separately study two types of properties of one common space: Geometric properties, and Behavioral properties.
Geometric modeling methods roughly follow this pipeline, and we will represent them with this blue box.
And behavioral modeling methods fall within this framework, and we will represent them with a green box.
In both types of methods, different levels of user input are normally supported.In behavioral modeling methods, the user normally specifies simulation scenarios that are run throughout long periods of time.In geometric modeling methods, the interaction is usually fasterIn most cases, there is no connection between both types of methods. That is, behavioral processes are not considered when modeling the geometry of urban spaces,And conversely, geometric processes are not used when modeling the behavior of an urban space.
Let me emphasize that there is thus an isolation between both types of methods, and this isolation results in:Functional disconnections between resultsGeometric models that do not resemble real-world spaces at a large scaleSimulations that only consider very coarse attributes of the geometry, as opposed to specific layouts and structures.
We have identified a number of advantages thatan integrated work between behavioral and geometric urban modeling methodswould bring to several applications.
Let me briefly present some of these applications.
One of them is urban visualization.Geometric modeling can assist in generating visualizations of urban scenarios that are more intuitive to different users.An initial approach that we have proposed with this purpose consists inInferring urban layouts from the values of a set of simulation variables at different time steps.
The image on top shows a traditional visualization of a behavioral urban variable (in this case, population density).The image at the bottom shows our visualization technique.The left image is an aerial view of an urban space at a time zero. The right image is produced by our system,by procedurally generating the structure of the city, and filling in the structure with fragments of images taken from the left photo.The behavioral information provided by the simulation is used to generate both the structure and the imagery of the city.
Here we see another example result.
Urban planning is a second application of integrating behavioral and geometric urban modeling.We are exploring this application in collaboration with Prof. Paul Waddell, from UC Berkeley.The goal of urban planning is to analyze and propose urban development scenarios.Let me use an example to show you how our proposed integration can contribute to this goal.
Let us use a scenario in which the average height of the buildings in the downtown of a city is increased. <click><click>Increasing the height of downtown buildings will result in more office space, which will eventually result in an increased number of jobs<click>,And increased population<click>.As a result of these events, <click><click> new housing will appear in accessible areas. These are both behavioral and geometric processes that are modeled concurrently, and can be specified and visualized interactively with our approach.This can be a useful tool for urban planners.
In this example the user specifies as input some landmarks consisting of the terrain, highways, parks, and location of the downtown.The system automatically computes as output a suitable distribution of the population and jobs <click>, a road network, parcels <click>, and buildings <click>.
There’s also applicability of concurrent behavioral and geometric modeling of urban spaces in content generation.Massive geometric models of cities are usefulto different products, and manual modeling is clearly not a reasonable approach.We want to go beyond simply creating random copies or even random procedural extensions of a sample model.Instead, we want to create models that actually make behavioral sense, so to speak. Models that resemble real world cities.In this particular example, notice how the 2D and 3D geometry of the model that our system created automatically, adapts to the population density of the urban space.
Here’s a wireframe render of the same model to give you an idea of the geometric complexity that we currently produce.
And here you see another much larger model that we created to test scalability. This is a city of 15 by 15 kilometers.Ultimately, we want to have an approach that can be scaled up from the neighborhood and city level to the county, state, and even country level[10 MINUTES]
Our second method for road generation creates the road network based on behavioral data.The method first computes a set of seeds considering the distribution of population and jobs, and the location of user-sketched highways.In these images you can see how the density of such seeds is higher in the areas of higher population and job density.
We then use each seed as an intersection of the arterial road network, and generate arterial road segments.
The very same algorithm is used again, but at a smaller scale, to generate streets.In this case, we create seeds along the previously generated arterial road segments, and expand street segments from these seeds.
These are examples of the road networks that we obtain for grid and radial patterns and variable levels of tortuosity.
To create blocks and parcels, we use a Voronoi-based approach. The number of parcels per block is determined based on socioeconomic data.
This is an example result.
We have done some preliminary work along this line.First, we have tested the stability of our integrated socioeconomic and geometric urban modeling system.To do this, we altered some design variables and compare the states to which the system converges after each perturbation. We expect to arrive at similar states.In this case we want to compare the top right image with the bottom left image. Their similarity indicates stability.
These are the results of an initial validation experiment.The picture on the left is a real city: Pacific Grove, California (USA).The picture on the right is the top view of the 3D model produced by our system, but with colors manually adjusted to match the real world color palette.The 3D model is produced with our behavioral simulation model, upon some input given by the user. The input includes the terrain, the location of the center,And the location of parks. Our behavioral system computes the population and job distribution, and procedurally generates all the geometry.Although our synthetic model does not perfectly match the details, the overall patterns are similar, as you can see in the zoomed regions.
One of the motivating questions is whether cities can be designed aiming to minimize the occurrence of undesirable meteorological phenomena.Possible approaches to answer this question require:-Fast…-Automatic…-Eventually, close loop simulation, that is, a simulation that automatically…
These are some preliminary results that show two meteorological variables: humidity and temperature.The images on the right show the changed in those variables.The first scenario that the weather simulation used was the real land cover and urban morphology of Indianapolis.The second scenario is a variation of the original scenario, in which we used our system to create a very large park covering a fraction of the south of indianapolis.The land cover and urban morphology data were recomputed by our system after the user-guided edit, and the weather model was run again.The result tells us that the park resulted in increased humidity and lower temperatures, not only in the area covered by the park, but in other nearby areas.
We presented a brief overview of the area of Urban Simulation and presented some recent work on Urban Visualization
