Urban Analytics & Information Fusion with CityGML

Technische Lehrstuhl für Geoinformatik Universität München
Urban Analytics & Information Fusion
with CityGML
Thomas H. Kolbe
Chair of Geoinformatics
Technische Universität München
thomas.kolbe@tum.de
March 7, 2014
Open Urban Information Model Seminar, Helsinki

7. 3. 2014
3D Model from Berlin Partner
T. H. Kolbe – Urban Analytics & Information Fusion with CityGML 2

What are the differences to the previous model?
(despite some colour variations)
3D Model from Google
7. 3. 2014 T. H. Kolbe – Urban Analytics & Information Fusion with CityGML
3

Answering by
• human:
• computer:
Answering by
• human:
• computer:
 
 
Queries on the 3D city model:
• How many buildings, monuments, trees?
• How many storeys?
• Where are entrances and exits?
• From which windows / roofs is plaza XY visible?
7. 3. 2014 T. H. Kolbe – Urban Analytics & Information Fusion with CityGML 4

3D City Modeling
► … is far more than
just 3D visualization
of reality
► in fact, geometry and
their graphical
appearance are
only two aspects
of an object
► Key aspect:
Semantic
Modeling

Contents
► Semantic 3D City Models
● Urban Information Fusion
● CityGML
► Application Example: Strategic Energy Planning
● Energy Atlas Berlin: Scale and Scope
● Estimation of Energy Demands for Individual Buildings
● Aggregation of Energy Demands
● Interactive 3D Visualization and Decision Support
► Live Demonstration
► Further Application Examples
● Environmental Noise Dispersion Simulation
● Vulnerability Analysis: Detonation Simulations

7. 3. 2014
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)
● energy related 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
>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
7. 3. 2014

Application Example:
Energy Atlas Berlin
7. 3. 2014
(+ London)

The Energy Turn: Reasons and Targets
► Climate change and natural disasters
● Reduction of greenhouse gas emissions
● Energy production with no or low CO2 emissions
► Finite resources of fossil fuels like gas, coal, or oil
● Energy production by sustainably available energy sources
► Security concerns in nuclear power production
● Exit from nuclear energy production in Germany
► Improving quality of life in cities
● Reduction of emissions such as fine dust,
noise, etc.
● Power generation with less / no emissions
in the inner cities
[Images: focus.de, naanoo.com]
14

Measures for Reorganization of Energy Supply
► Centralized vs. decentralized energy production
● e.g. large power stations vs. block heat and power plants
► Exploitation of regenerative & natural energy
● Solar thermal & Photovoltaic energy
● Geothermal energy
► Extension, construction, alternative usages
of supply / distribution infrastructures
► Measures to increase energy efficiency
● e.g. building retrofitting; always affects individual
components or buildings in the end
► Introducing large amount of e-mobility
► Influencing of consumer behaviors

Energy Atlas Berlin
► Collaboration project (2.5M€) partially funded by the
European Institute of Innovation and Technology EIT
► located within the Knowledge & Innovation Center
for Climate Change and Mitigation (Climate KIC)
● PI: Chair of Geoinformatics,
● German Research Centre for
Geosciences Potsdam (GFZ)
● Vattenfall Europe Berlin AG
● GASAG AG
● Berlin Partner GmbH
● Berlin Senate of Economics,
Technology and Research
● City District Administration
Charlottenburg-Wilmersdorf in Berlin
► Partners:
Berlin University of Technology:
● Innovation Center Energy
● Institute for Geodesy and
Geoinformation Science
● PI: Instit. for Energy Technologies
● Institute for Energy and Automation
Technology
● Institute for Architecture
● Institute for Technology and
Management
● Center for Technology & Society
16

Goals of the Energy Atlas Berlin
► 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 measures
and visualization of their effects
► 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
17

Scale Levels of the Energy Atlas
► City
► District
► Quarter / Block
► Building / Street
► Appartement
► Room
Generalisation / Aggregation
Resolution / Level of Detail
18

Energy Atlas System Design
Geoinformatics/
Standards developer
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
Housing
Companies
Energy
service
provider
Citizens
… many
more modules
Consulting Development (GIS-Developer / Simulation Experts)
GIS
Specialists
19

7. 3. 2014
Energy Demand
Estimation

Correlation Consumption  Building param’s
Building data Consumption data
• Electricity
• Water
• Gas
• (Remote) Heating
Only available for a few
households (detailed
data only where Smart
Meters are installed)
• Volume [m³]
• Floor space [m²]
• Building type
• Building usage
• Year of construction
• (renovation state)
• Number of habitants
• 3D City Model
• Geo Base Data
Correlation
What is the
relation of
consumption
with specific
building
characteristics?
Full coverage
of entire cities!
21

Energy Demand Estimation (I)
GIS
3D City Model +
Geo Base Data
Estimation of the
individual energy
demand for every
single building
Quarter level
Estimation
of the
energy demand
District level
City level
Aggregation
Correlation
function +
22

Energy Demand Estimation (II)
GIS
3D City Model +
Geo Base Data
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
Quarter level
Estimation
of the
energy demand
District level
City level
Aggregation
! !
23

Estimation of Heating Energy Demand
► Building-specific and city-wide calculation based on
algorithms of the Institut Wohnen und Umwelt (IWU)
► Based on the virtual 3D city model and official geobase
data within the Energy Atlas Berlin
Climate and
environment
conditions
Correlation
Building Information
• Geometry
• Usage
• Construction
• Rehabilitation
• Residents
• Apartments
Energy Demand
• Electricity
• Warm Water
• Heating
24

Determination of Input Values
► Climate conditions: according to VDI 2067 for Berlin
► Global radiation: standard values from the IWU
► Building geometry: calculated from 3D city model
● Energy reference area
● Building volume
● Boundary surface areas (walls, windows, roof, ground)
► Number of storeys: calculated from 3D city model
► Building usage: taken from 3D city model (geobase data)
► Building construction: Estimated using building age class
● Heat transmission coefficient (U-Value) of the components
● Energy transmittance (g-Value) of the windows
► Rehabilitation state: definition of rehabilitation classes

Calculation of Heating Energy Demand
► The energy demand of a building QH is the difference of
the heat losses and heat gains:
QH = QV - QG [kWh/a]
QV heat losses [kWh/a]
QG usable heat gains [kWh/a]
► Calculation of heat losses
● through the boundary surfaces
● due to periodical airing
► Calculation of heat gains
● sunlight irradiation
● internal heat sources
[http://www.lambdaplus.de]
26

Estimated Heating Energy Demand
Estimated Energy
Demand [kwh/a]
27

Estimation of Electrical Energy Demand
► Building-specific and city-wide estimation based on average
electrical energy consumption statistics for households,
published by company Vattenfall
► Household data are estimated from the virtual 3D city model
and geobase data within the Energy Atlas Berlin
Climate and
environment
conditions
Correlation
• Geometry
• Usage
• Construction
• Rehabilitation
• Residents
• Apartments
Energy Demand
• Electricity
• Warm Water
• Heating
[PhD Work of Robert Kaden, 2013]
28

Estimation of Input Values
► # residents: estimated from the given population of a block
and the building volume of the buildings within the block
► Number of Apartments: Estimated by using the empirically
estimated ratio of the number of apartments per building
volume and the volume of a building

Estimation of Input Values
► # residents: estimated from the given population of a block
Validation of the estimated number of inhabitants and
and the building volume of the buildings apartments within per building:
the block
► Number of Apartments: Estimated by using the empirically
For district Mitte: Σ Residents / Σ Apartments = 1.61
estimated ratio of the number of apartments per building
volume and the volume of a building
[Amt für Statistik Berlin Brandenburg, 2011]
30

Calculation of Electrical Energy Demand
► Electrical energy demand of a building is estimated based
on the average annual consumption values of households
and the number of residents per household
► Distribution of the residents
per building to the residential
units of the building
7,646768624
7,548100641 Households in Mitte - Berlin
[Vattenfall, 2012]
62,60483473
22,24963
1 person
2 persons
3 persons
4 or higher
[Amt für Statistik Berlin Brandenburg, 2011]
31

Estimation of Energy Demand for Warm Water
► Building-specific and city-wide calculation bases on
algorithms of the Institut Wohnen und Umwelt (IWU)
► Based on the virtual 3D city model and official geobase
data within the Energy Atlas Berlin
Climate and
environmen
t conditions
Correlation
• Geometry
• Usage
• Construction
• Rehabilitation
• Residents
• Apartments
Energy Demand
• Electricity
• Warm Water
• Heating
[PhD Work of Robert Kaden, 2013]
32

Exploration of Building Energy Parameters

Aggregating Energy Indicators for Districts

Analysis of Saving Potentials by Retrofitting
► Heating energy demand depends on the construction type
● U values of components: determined using the building age class
and the building type taken from the 3D city model
● g values of the windows: determined using the building age class
and the building type taken from the 3D city model
● definition of different (and possible) retrofitting levels for each
building by variations of U and g values
BAK Zeitraum Durchschn.U-Wert
Wand
BAK Zeitraum Durchschn.U-Wert
W/(m2K)
Durchschn.U-Wert
Fenster
W/(m2K)
Durchschn.
g-Wert Fenster
Durchschn. U-Wert
Dach
Durchschn.
g-Wert Fenster
W/(m2K)
Durchschn. U-Wert
Durchschn. U-Wert
Kellerdecke
Durchschn.
U-Wert
Fenster
W/(m2K)
Durchschn.
W/(m2K)
Fenster-Wand-
Flächen-verhältnisse
mittleres
Fenster-
Wand-
Flächenverh
Fenster-Wand-
Durchschn.
U-Wert
Kellerdecke
W/(m2K)
ältnis
Wand
BAK Zeitraum Durchschn.
W/(m2K)
Durchschn.U-Wert
Fenster
W/(m2K)
Dach
W/(m2K)
Durchschn. U-Wert
Durchschn.
U-Wert
Kellerdecke
W/(m2K)
U-Wert
Wand
W/(m2K)
g-Wert
Fenster
Dach
1 bis 1918 1,70 2,7 0,76 1,50 1,20 0,25 – 0,34 0,30
1919 – 1945 W/(m2K)
2 1,70 2,7 0,76 1,50 1,20 0,20 – 0,35 0,25
3 1946 – 1961 1,40 2,7 0,76 1,30 1,00 0,20 – 0,31 0,23
4 1962 – 1974 1,20 2,7 0,76 1,10 0,84 0,20 – 0,42 0,28
5 1975 – 1993 0,80* 2,7 0,76 0,45 0,60 0,20 – 0,40 0,33
6 1994 – 2012 0,40* 1,7 0,72 0,30 0,40 0,30 – 0,50 0,35
mittleres
Fenster-
Wand-
Flächenverh
Fenster-
Wand-
ältnis
1 bis 1918 1,70 2,7 0,76 1,50 1,20 0,25 – 0,34 0,30
2 1919 – 1945 1,70 2,7 0,76 1,50 1,20 0,20 – 0,35 3 1946 – 1961 1,40 2,7 0,76 1,30 1,00 0,20 – 0,31 0,23
4 1962 – 1974 1,20 2,7 0,76 1,10 0,84 0,20 – 0,42 0,28
5 1975 – 1993 0,80* 2,7 0,76 0,45 0,60 0,20 – 0,40 0,33
6 1994 – 2012 0,40* 1,7 0,72 0,30 0,40 0,30 – 0,50 0,35
mittleres
Fenster-
Wand-
Flächenver
hältnis
1 bis 1918 1,70 2,7 0,76 1,50 1,20 0,25
– 0,34 0,30
2 1919 – 1945 1,70 2,7 0,76 1,50 1,20 0,20 – 0,35 0,25
3 1946 – 1961 1,40 2,7 0,76 1,30 1,00 0,20 – 0,31 0,23
4 1962 – 1974 1,20 2,7 0,76 1,10 0,84 0,20 – 0,42 0,28
5 1975 – 1993 0,80* 2,7 0,76 0,45 0,60 0,20 – 0,40 0,33
6 1994 – 2012 0,40* 1,7 0,72 0,30 0,40 0,30 – 0,50 0,35
37

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

7. 3. 2014
Live Demo
Energy Atlas

Screenshot of the Energy Atlas Webclient

7. 3. 2014
Noise Dispersion
Simulation and Mapping

Environmental Noise Dispersion Simulation
CityGML is 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
7. 3. 2014
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

Vulnerability Analysis
(Detonation Simulation)
7. 3. 2014

‘Controlled‘ Blast of discovered
unexploded Bomb from World War II
Detonation in Munich, District Schwabing, 2012
Unexploded American 500 lbs Bomb (120kg TNT)
Evacuation of 2500 citizens
Source:
Münchner
Abendzeitung
Bildzeitung
Source: Google Maps
Chair of Metal Structures Prof. Martin Mensinger, Stefan Trometer 45

‘Controlled‘ Blast of discovered
unexploded Bomb from World War II
Detonation in Munich, District Schwabing, 2012
Chair of Metal Structures Prof. Martin Mensinger, Stefan Trometer 46

Coming to the end . . .
11. 2. 2011

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 devel.)
● 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
● typ. 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
● 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

Urban Analytics & Information Fusion with CityGML

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