Semantic 3D City Models with CityGML

Technische Universität MünchenLehrstuhl für Geoinformatik
Semantic 3D City Models with CityGML
for Urban Analytics and Cross Sector Data Integration
Prof. Dr. Thomas H. Kolbe
Chair of Geoinformatics
Technische Universität München
thomas.kolbe@tum.de
January 22, 2015
ICGC 3D City Models Workshop,
Barcelona

222.1.2015
Model Entities
(Resources,
Objects)
Actors (Agents,
Stakeholders,
Citizens)
Processes
(Activities,
Actions, Flows)
City Modeling for Smart Cities
T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics
represented by
City System

322.1.2015
Today: Separate Modeling by Sectors
Energy
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning
Mobility
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning Ecology
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning
Economy
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning
City System

422.1.2015
Linking Sectors creates a Lattice of Models
Energy
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning
Mobility
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning Ecology
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning
Economy
• Commu-
nity
• Models
• Indicators
• Evalua
-tion
• Planning
City System

Lattice of Sector Models
22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 5
► n Sectors  potentially n2 connections!
► difficult to express, to maintain, and to keep consistent
Energy
Economy
. . .Ecology
Mobility

What if we could link to One Common Model?
► n Sectors  n connections!
► Sector models can be linked via the Common Model
► Sector models need to be aligned with the Common City
System Model  high degree of coherence required
Common
City
System
Model
Energy
Economy
. . .Ecology
Mobility

722.1.2015
Is there such an integrative model? Candidates?
City System
Common
City
System
Model
Energy
Economy
. . .Ecology
Mobility
repre-
sented
by

22.1.2015
Semantic
3D City Models

Spatio-semantic Modeling of Our World
► many relevant urban entities are physical objects
► physical objects occupy space in the real world
● partitioning of occupied real space  discrete objects
● criteria for subdivision: thematic classification into different
topographic elements like buildings, streets, trees etc.
► spatio-semantic representation
of the relevant geoinformationen
● modeling of the city & its constituents
● classified objects with thematic data
● spatial aspects: location, shape, extent
► different, discrete levels of detail (LODs)
► real world is 3D  semantic 3D city models

3D Decomposition of Urban Space
► City is decomposed into meaningful objects with clear
semantics and defined spatial and thematic properties
● buildings, roads, railways, terrain, water bodies, vegetation, bridges
● buildings may be further decomposed into different storeys
(and even more detailed into apartments and single rooms)
● application specific data are associated with the different objects
Image: Paul Cote, Harvard Graduate School of Design

City Geography Markup Language – CityGML
Application independent Geospatial Information Model
for semantic 3D city and landscape models
► comprises different thematic areas
(buildings, vegetation, water, terrain,
traffic, tunnels, bridges etc.)
► Internat‘l Standard of the Open Geospatial Consortium
● V1.0.0 adopted in 08/2008; V2.0.0 adopted in 3/2012
► Data model (UML) + Exchange format (based on GML3)
CityGML represents
► 3D geometry, 3D topology, semantics, and appearance
► in 5 discrete scales (Levels of Detail, LOD)

Semantic 3D City Model of Berlin
22.1.2015
>550,000 buildings;
• fully-automatically generated
from 2D cadastre footprints &
airborne laserscanning data.
• textures (automatically
extracted from aerial images)
• semantic information (includes
data from cadastre)
• 3D utility networks from the
energy providers
• modeled according to CityGML www.virtual-berlin.de
T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 13

Attaching Diverse Information Content
► The given structuring of the CityGML model enables to
relate domain specific application data to entities of
the real world by linking it with the ID of the corresponding
geoobject in an unambiguous way
● requires that the structuring of the geodata is fitting to
(coherent with) the application
14
Object BLDG_234ae23aa
Class: Building
Number of Storeys: 5
Adresses: …
Stable object
ID value over
the lifetime of
the object!
22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics

Semantic 3D City Model as Integration Platform

(Inter)national Usage / Availability of CityGML
► Cities / Municipalities
● e.g. almost all German cities with 3D city models; Rotterdam, Zürich,
Geneva, Paris, Marseille, Helsinki, Istanbul, Vancouver, Montreal,
Kuala Lumpur, Yokohama, Singapore, Abu Dhabi, and many more;
however, few implementations in the USA so far (e.g. Blacksburg)
► Organisations
● e.g. IGN France, Ordnance Survey UK, State Mapping Agencies of
Bavaria, BaWü, Hesse, RLP, NRW, BIMTAS in Istanbul, many
companies, research institutes, and universities
► CityGML is reference model in the European
INSPIRE initiative ( full EU coverage)
● INSPIRE building model is based on CityGML
► The official national and municipal 3D geoinformation
standards of Germany, The Netherlands base on CityGML

22.1.2015
(Some) CityGML
Details

CityGML is a Modular Standard
18
AppearanceModule
GenericsModule
CityGMLCoreModule
Bridge Module
Building Module
CityFurniture Module
LandUse Module
Relief Module
Transportation Module
Tunnel Module
Vegetation Module
Waterbody Module
CityObjectGroup Module
Noise ADE
Energie ADE
Many more ADEs…..
Thematic
Modules
ADEs

LOD 0 – Regional model
 2.5D Digital Terrain Model
LOD 1 – City / Site model
 “block model“ w/o roof structures
 textured, differenciated roof structures
 detailed architecture model
LOD 4 – Interior model
 “walkable“ architecture models
Multi-scale modeling: 5 levels of details

Thematic Modeling in CityGML
ExternalReference
- informationSystem: anyURI
- externalReference:
ExternalObjectReferenceType
<<FeatureCollection>>
CityModel **
…
loD0-4GeometryProperty
<<Geometry>>
gml::_Geometry loD0-4GeometryProperty
<<Feature>>
_Transportation
Object
<<Feature>>
_Abstract
Building
<<Feature>>
ReliefFeature
<<Feature>>
_WaterBody
<<Feature>>
_Vegetation
<<Feature>>
_CityObject
<<Feature>>
gml::_Feature

22.1.2015
Application Example:
Energy Atlas Berlin
(+ London)

Goals of the Energy Atlas Berlin
► Information backbone for multiple analyses & simulations
● Estimation of heating, electrical, and warm water energy demands
● Energetic building characteristics and rehabilitation potentials
● Design of an optimal electricity network, taking into account the
current demand and load peaks
● Usage of geothermal and solar energy potentials
► Tool for holistic energy planning
● Analysis and representation of the
actual state of objects and their energy-
relevant parameters within a city
● Investigation and balancing of options
and measures
● Decision support for various actions and
visualization of their effects
22

Scale Levels of the Energy Atlas
► City
► District
► Quarter / Block
► Building / Street
► Appartement
► Room
Generalisation/Aggregation
Resolution/LevelofDetail
23

Energy Atlas System Design
3D City Model
+ Energy
ADE
Acquisition
+
Conversion
+
Editing
of Cadastre
Data
Urban Analytics Toolkit
Visualization
+
Reporting
- What-if
scenarios
- Application
data acquisition
City
(London)
City
City
Cities
(e.g. Berlin)
Solar Potential
Analyis
Heating
Consumption
Estimation
Specific energetic
environmental
technology
issues
Stakeholder
Cities
Energy
Supplier
Energy
service
provider
Citizens
Housing
Companies
Consulting Development (GIS-Developer / Simulation Experts)
Geoinformatics/
Standards developer
… many
more modules
GIS
Specialists
24

22.1.2015
(Heating)
Energy Demand
Estimation

Correlation Consumption  Building param’s
Consumption data
• Electricity
• Water
• Gas
• (Remote) Heating
Only available for a few
households (detailed
data only where Smart
Meters are installed)
• 3D City Model
• Geo Base Data
Building data
• Volume [m³]
• Floor space [m²]
• Building type
• Building usage
• Year of construction
• (renovation state)
• Number of habitants
Full coverage
of entire cities!
What is the
relation of
consumption
with specific
building
characteristics?
Correlation
26

Energy Demand Estimation (I)
3D City Model +
Geo Base Data
Estimation
of the
energy demand
GIS
District level
City level
Quarter level
Estimation of the
individual energy
demand for every
single building
Aggregation
Correlation
function+
27

Energy Demand Estimation (II)
3D City Model +
Geo Base Data
GIS
Estimation of the
individual energy
demand for every
single building
Correlation
function+
Changes to the
city model
according
to planned /
possible measures
Impacts on the
energy demand
can be directly
estimated and
compared with the
current status
Estimation
of the
energy demand
District level
City level
Quarter level
Aggregation
! !
28

Estimation of Heating Energy Demand
► Building-specific and city-wide calculation based on
German Standard DIN 18599
► Based on the virtual 3D city model and official geobase
data within the Energy Atlas Berlin
Correlation
Building Information
• Geometry
• Usage
• Construction
• Rehabilitation
• Residents
• Apartments
Energy Demand
• Electricity
• Warm Water
• Heating
Climate and
environment
conditions
29

Exploration of Building Energy Parameters

Aggregating Energy Indicators for Districts

Energy Atlas:
Information Fusion
Energy Atlas
Energy demands
analyses
Energy savings
potentials
Geothermal potential
analysis
Solar potential
analysis
Infrastructure
analysis

22.1.2015
Live Demo
Energy Atlas

Screenshot of the Energy Atlas Webclient

22.1.2015
Noise Dispersion
Simulation and Mapping

Environmental Noise Dispersion Simulation
CityGML is the basis for the computation of the noise
immission maps for the state of North-Rhine Westphalia
● Background: EU directive on reduction of environmental noise
● Cooperation project of Univ. Bonn, state NRW, and companies
● Provision and exchange of all data exclusively in CityGML and
corresponding Web Services (WFS, WCS, WMS):
● 8.6 million 3D buildings in LOD1 (18.6 million citizens in NRW!)
● 3D road network NRW in LOD0 (based on 2D models in
OKSTRA, ATKIS & DTM5), extended by those properties relevant
ro noise dispersion simulation
● 3D railway network NRW in LOD0 (based on ATKIS, DTM5)
● 3D noise barriers in LOD1
● DTM5 (a 10m raster was used)

Computation of Noise Immission Maps
22.1.2015
Noise immission maps
for reporting to the EU
(via WMS Service)
3D Model in
CityGML (via
WFS Service)
DTM 10m
Raster (via
WCS Service)
Noise
propagation
simulation
T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 39

22.1.2015
Vulnerability Analysis
(Detonation Simulation)

Chair of Metal Structures Prof. Martin Mensinger, Stefan Trometer 41
‘Controlled‘ Blast of discovered
unexploded Bomb from World War II
Detonation in Munich, District Schwabing, 2012
Source:
Münchner
Abendzeitung
Bildzeitung
Unexploded American 500 lbs Bomb (120kg TNT)
Evacuation of 2500 citizens
Source: Google Maps

Chair of Metal Structures Prof. Martin Mensinger, Stefan Trometer 42
Detonation in Munich, District Schwabing, 2012
‘Controlled‘ Blast of discovered
unexploded Bomb from World War II

22.1.2015
Coming to the end . . .

Conclusions
► Semantic 3D City Models ( Urban Information Models)
● are an appropriate reference model and data platform to attach /
link domain specific urban information across different disciplines
● Semantic 3D city models often are provided by authoritative
sources (municipal agencies, state & national mapping agencies)
 full coverage of the urban space, high reliability, stability
Google 3D models, Open Streetmap are not suitable !!
● facilitate comprehensive analyses on the urban scale in the fields of
e.g. energy assessment, environmental simulation, urban planning
● can accumulate knowledge (including analyses results)
► Interoperability is key for information integration
● OGC‘s CityGML defines the semantic model + exchange format
● CityGML is an Open, vendor independent Standard
● CityGML allows for 3D visualizations AND thematic analyses

... and what about BIM / IFC ?
► CityGML is complementary to IFC
● both, IFC and CityGML are information models
● IFC: building objects (other man-made objects under development)
● CityGML: man-made and natural objects; geomorphology
► IFC‘s modeling approach is tailored to support the
planning, design, construction, and operation of buildings
● one, high level of detail
● typically only available for newly planned / constructed buildings
► CityGML‘s modeling approach is tailored to describe the
real world from observations / measurements
● in five levels of detail; conversion of IFC  CityGML is possible
● automated data acquisition methods; coverage of entire cities
● very large datasets can be managed within GIS, geodatabases

References
► R. Kaden, T. H. Kolbe: City-Wide Total Energy Demand Estimation of Buildings us-ing Semantic 3D
City Models and Statistical Data. In: Proc. of the 8th International 3D GeoInfo Conference, 28.-29. 11.
2013 in Istanbul, Turkey, ISPRS Annals of the Photo-grammetry, Remote Sensing and Spatial
Information Sciences, Volume II-2/W1, 2013
Click for article download
► A. Krüger, T. H. Kolbe: Building Analysis for Urban Energy Planning Using Key Indicators on Virtual
3D City Models - The Energy Atlas of Berlin. In: Proceedings of the ISPRS Congress 2012 in
Melbourne, International Archives of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, Volume XXXIX-B2, 2012
► D. Carrion, A. Lorenz, T. H. Kolbe: Estimation of the Energetic Rehabilitation State of Buildings for
the City of Berlin Using a 3D City Model Represented in CityGML. In: Proceedings of the 5th Intern.
Conference on 3D Geo-Information 2010 in Berlin, International Archives of Photogrammetry,
Remote Sensing, and Spatial Information Sciences, Vol. XXXVIII-4/W15
► T. H. Kolbe: Representing and Exchanging 3D City Models with CityGML. In: J. Lee, S. Zlatanova
(Eds.), 3D Geo-Information Sciences, Proceedings of the 3rd Intern. Workshop on 3D Geo-
Information in Seoul, Korea. Springer, Berlin, 2008

Credits
► The Energy Atlas project has been funded
by Climate-KIC of the European Institute
for Innovation and Technology (EIT)
► The 3D City Model of Berlin was provided
by Berlin Partner GmbH.
Its creation was supported by the European
Regional Development Fund (ERDF) and the
Berlin Senate of Economy, Technology &
Women‘s Affairs
► The 3D City Model of London‘s District
Bromley-By-Bow was generated from
building footprints from Ordnance Survey
Mastermap and a DSM and DTM from Infoterra

Semantic 3D City Models with CityGML

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