Semantic 3D City Models with CityGML
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The document presents an overview of semantic 3D city models using CityGML for urban analytics and data integration, emphasizing the need for an integrative model that links various sector models for enhanced coherence. It outlines how city models can be decomposed into meaningful objects with defined spatial properties and discusses applications such as energy demand estimation and environmental noise simulation. The document also highlights the widespread adoption of CityGML across various cities globally and its role in supporting urban planning and analytics.
![Technische Universität MünchenLehrstuhl für Geoinformatik
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)
22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics
• 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](https://image.slidesharecdn.com/20150122presentacio3dcitymodels-150123023244-conversion-gate02/85/Semantic-3D-City-Models-with-CityGML-26-320.jpg)
Semantic 3D City Models with CityGML
- 1. 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 [email protected] January 22, 2015 ICGC 3D City Models Workshop, Barcelona
- 2. Technische Universität MünchenLehrstuhl für Geoinformatik 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
- 3. Technische Universität MünchenLehrstuhl für Geoinformatik 322.1.2015 Today: Separate Modeling by Sectors T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 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
- 4. Technische Universität MünchenLehrstuhl für Geoinformatik 422.1.2015 Linking Sectors creates a Lattice of Models T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 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
- 5. Technische Universität MünchenLehrstuhl für Geoinformatik 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
- 6. Technische Universität MünchenLehrstuhl für Geoinformatik What if we could link to One Common Model? 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 6 ► 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. Technische Universität MünchenLehrstuhl für Geoinformatik 722.1.2015 Is there such an integrative model? Candidates? T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics City System Common City System Model Energy Economy . . .Ecology Mobility repre- sented by
- 8. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 Semantic 3D City Models
- 9. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 9
- 10. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 10
- 11. Technische Universität MünchenLehrstuhl für Geoinformatik 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) 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 11
- 12. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 12
- 13. Technische Universität MünchenLehrstuhl für Geoinformatik 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
- 14. Technische Universität MünchenLehrstuhl für Geoinformatik 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
- 15. Technische Universität MünchenLehrstuhl für Geoinformatik Semantic 3D City Model as Integration Platform 1522.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics
- 16. Technische Universität MünchenLehrstuhl für Geoinformatik (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 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 16
- 17. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 (Some) CityGML Details
- 18. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics
- 19. Technische Universität MünchenLehrstuhl für Geoinformatik LOD 0 – Regional model 2.5D Digital Terrain Model LOD 1 – City / Site model “block model“ w/o roof structures LOD 2 – City / Site model textured, differenciated roof structures LOD 3 – City / Site model detailed architecture model LOD 4 – Interior model “walkable“ architecture models Multi-scale modeling: 5 levels of details 1922.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics
- 20. Technische Universität MünchenLehrstuhl für Geoinformatik 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 2022.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics
- 21. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 Application Example: Energy Atlas Berlin (+ London)
- 22. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics ► 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
- 23. Technische Universität MünchenLehrstuhl für Geoinformatik Scale Levels of the Energy Atlas 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics ► City ► District ► Quarter / Block ► Building / Street ► Appartement ► Room Generalisation/Aggregation Resolution/LevelofDetail 23
- 24. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics GIS Specialists 24
- 25. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 (Heating) Energy Demand Estimation
- 26. Technische Universität MünchenLehrstuhl für Geoinformatik 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) 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics • 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
- 27. Technische Universität MünchenLehrstuhl für Geoinformatik Energy Demand Estimation (I) 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 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
- 28. Technische Universität MünchenLehrstuhl für Geoinformatik Energy Demand Estimation (II) 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 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
- 29. Technische Universität MünchenLehrstuhl für Geoinformatik Estimation of Heating Energy Demand 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics ► 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
- 30. Technische Universität MünchenLehrstuhl für Geoinformatik Exploration of Building Energy Parameters 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 30
- 31. Technische Universität MünchenLehrstuhl für Geoinformatik Exploration of Building Energy Parameters 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 31
- 32. Technische Universität MünchenLehrstuhl für Geoinformatik Aggregating Energy Indicators for Districts 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 32
- 33. Technische Universität MünchenLehrstuhl für Geoinformatik Aggregating Energy Indicators for Districts 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 33
- 34. Technische Universität MünchenLehrstuhl für Geoinformatik Energy Atlas: Information Fusion 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 34 Energy Atlas Energy demands analyses Energy savings potentials Geothermal potential analysis Solar potential analysis Infrastructure analysis
- 35. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 Live Demo Energy Atlas
- 36. Technische Universität MünchenLehrstuhl für Geoinformatik Screenshot of the Energy Atlas Webclient 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 36
- 37. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 Application Example: Noise Dispersion Simulation and Mapping
- 38. Technische Universität MünchenLehrstuhl für Geoinformatik 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) 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 38
- 39. Technische Universität MünchenLehrstuhl für Geoinformatik 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
- 40. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 Application Example: Vulnerability Analysis (Detonation Simulation)
- 41. 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
- 42. 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
- 43. Technische Universität MünchenLehrstuhl für Geoinformatik 22.1.2015 Coming to the end . . .
- 44. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 44
- 45. Technische Universität MünchenLehrstuhl für Geoinformatik ... 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 45
- 46. Technische Universität MünchenLehrstuhl für Geoinformatik 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 Click for article download ► 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 Click for article download ► 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 Click for article download 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 46
- 47. Technische Universität MünchenLehrstuhl für Geoinformatik 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 22.1.2015 T. H. Kolbe – Semantic 3D City Models with CityGML for Urban Analytics 47