Urban Analytics & Information Fusion with CityGML

Technische Universität München Lehrstuhl für Geoinformatik

Urban Analytics & Information Fusion

with CityGML

Thomas H. Kolbe Chair of Geoinformatics Technische Universität München

[email protected] March 7, 2014 Open Urban Information Model Seminar, Helsinki


7. 3. 2014

3D Model from Berlin Partner

2 T. H. Kolbe – Urban Analytics & Information Fusion with CityGML


7. 3. 2014 3

What are the differences to the previous model?

(despite some colour variations)

3D Model from Google

T. H. Kolbe – Urban Analytics & Information Fusion with CityGML


7. 3. 2014 T. H. Kolbe – Urban Analytics & Information Fusion with CityGML 4

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?

Answering by

• human:

• computer:

Answering by

• human:

• computer:


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

7. 3. 2014

>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 – Urban Analytics & Information Fusion with CityGML 12


7. 3. 2014

Application Example:

Energy Atlas Berlin

(+ 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

7. 3. 2014 T. H. Kolbe – Urban Analytics & Information Fusion with CityGML

[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

● PI: Chair of Geoinformatics, Technische Universität München

● 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

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


► 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)

► Partners:

16


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 measures

and visualization of their effects

17


Scale Levels of the Energy Atlas


► City

► District

► Quarter / Block

► Building / Street

► Appartement

► Room

Ge

ne

ralis

atio

n / A

gg

reg

atio

n

Re

so

lutio

n / L

eve

l of D

eta

il

18


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

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

What is the

relation of

consumption

with specific

building

characteristics?


Full coverage

of entire cities!

Correlation

21


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

Aggre

gation

Correlation

function +

22


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

Aggre

gation ! !

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

Correlation

Building Information

• Geometry

• Usage

• Construction

• Rehabilitation

• Residents

• Apartments

Energy Demand

• Electricity

• Warm Water

• Heating

Climate and

environment

conditions

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:

► Calculation of heat losses

● through the boundary surfaces

● due to periodical airing

► Calculation of heat gains

● sunlight irradiation

● internal heat sources


QH = QV - QG [kWh/a] QV heat losses [kWh/a]

QG usable heat gains [kWh/a]

[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

Correlation


• Geometry

• Usage

• Construction

• Rehabilitation

• Residents

• Apartments

Energy Demand

• Electricity

• Warm Water

• Heating

Climate and

environment

conditions

[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

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


Validation of the estimated number of inhabitants and

apartments per building:

For district Mitte: ∑ Residents / ∑ Apartments = 1.61

[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


[Vattenfall, 2012]

62,60483473 22,24963

7,646768624

7,548100641 Households in Mitte - Berlin

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

Correlation


• Geometry • Usage

• Construction

• Rehabilitation • Residents

• Apartments

Energy Demand

• Electricity

• Warm Water

• Heating

Climate and

environmen

t conditions

[PhD Work of Robert Kaden, 2013]

32


Exploration of Building Energy Parameters



Exploration of Building Energy Parameters



Aggregating Energy Indicators for Districts



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

W/(m2K)

Durchschn.U-

Wert Fenster

W/(m2K)

Durchschn.

g-Wert Fenster

Durchschn. U-

Wert Dach

W/(m2K)

Durchschn. U-

Wert

Kellerdecke

W/(m2K)

Fenster-Wand-

Flächen-

verhältnisse

mittleres

Fenster-

Wand-

Flächenverh

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 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

BAK Zeitraum Durchschn.U-

Wert Wand

W/(m2K)

Durchschn.U-

Wert Fenster

W/(m2K)

Durchschn.

g-Wert Fenster

Durchschn. U-

Wert Dach

W/(m2K)

Durchschn. U-

Wert

Kellerdecke

W/(m2K)

Fenster-Wand-

Flächen-

verhältnisse

mittleres

Fenster-

Wand-

Flächenverh

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 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

BAK Zeitraum Durchschn.

U-Wert

Wand

W/(m2K)

Durchschn.

U-Wert

Fenster

W/(m2K)

Durchschn.

g-Wert

Fenster

Durchschn.

U-Wert

Dach

W/(m2K)

Durchschn.

U-Wert

Kellerdecke

W/(m2K)

Fenster-

Wand-

Flächen-

verhältnisse

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


Energy Atlas Energy demands

analyses

Energy savings

potentials

Geothermal potential

analysis

Solar potential

analysis

Infrastructure

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

T. H. Kolbe – Urban Analytics & Information Fusion with CityGML 43


7. 3. 2014


Vulnerability Analysis

(Detonation Simulation)

Technische Universität München

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

Source:

Münchner

Abendzeitung

Bildzeitung

Unexploded American 500 lbs Bomb (120kg TNT)

Evacuation of 2500 citizens Source: Google Maps

Technische Universität München

Chair of Metal Structures Prof. Martin Mensinger, Stefan Trometer 46

Detonation in Munich, District Schwabing, 2012

‘Controlled‘ Blast of discovered

unexploded Bomb from World War II


11. 2. 2011

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 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



http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-2-W1/163/2013/isprsannals-II-2-W1-163-2013.html

http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B2/145/2012/isprsarchives-XXXIX-B2-145-2012.html






http://www.isprs.org/proceedings/xxxviii/4-w15/Paper_ISPRS/Oral/6_3DGeoInfo2010_157_Carrion_Energetic_Rehabilitation.pdf






http://mediatum.ub.tum.de/doc/1145752/1145752.pdf







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


Data & Analytics

Urban Analytics & Information Fusion with CityGML