Semantic 3D City Models with CityGML

Technische Universität München Lehrstuhl 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

[email protected] January 22, 2015 ICGC 3D City Models Workshop, Barcelona


2 22.1.2015

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


3 22.1.2015

Today: Separate Modeling by Sectors


En

erg

y

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning

Mo

bili

ty

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning E

co

log

y

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning

Eco

no

my

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning

City System


4 22.1.2015

Linking Sectors creates a Lattice of Models


En

erg

y

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning

Mo

bili

ty

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning E

co

log

y

• Commu-nity

• Models

• Indicators

• Evalua -tion

• Planning

Eco

no

my

• 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


7 22.1.2015

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

15 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics


(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



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(Some) CityGML

Details


CityGML is a Modular Standard

18

Ap

pe

ara

nc

e M

od

ule

Gen

eri

cs

Mo

du

le

Cit

yG

ML

Co

re M

od

ule

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

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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

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


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(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

Aggre

gation

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

Aggre

gation ! !

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)

Technische Universität München

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

Technische Universität München

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



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


Semantic 3D City Models with CityGML

Technology

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