Semantic 3D City Models for Urban Information Fusion and City Lifecycle Management

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1 Technische Universität Berlin Semantic 3D City Models for Urban Information Fusion and City Lifecycle Management Thomas H. Kolbe Director Institute for Geodesy & Geoinformation Science Technische Universität Berlin July 4th, 2012 Earth Systems Engineering Symposium Newcastle

2 Who are we? Institute for Geodesy and Geoinformation Science (IGG), Chair of Methods of Geoinformation Technology Team consisting of 7 researchers / PhD students, 3 techn. staff Main research areas: Spatio-semantic modeling, analyses and databases 3D object recognition, interpretation, and 3D geovisualization Strong focus on Virtual 3D City and Landscape Models, Urban Information Modeling & Fusion Indoor Navigation (spatial modeling, localization methods, orientation support) Originator and co-leader in the development of the international standards CityGML, Web 3D Service, IndoorGML of the Open Geospatial Consortium 2 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

3 City Lifecycle Management (in analogy to product lifecycle management) is about the conception, design, construction, usage, maintenance, and disposal of urban assets many assets are directly related to physical objects in the real world (e.g. buildings, bridges, roads, technical infrastructures, trees) requires a comprehensive documentation of the status of (and plans about) the assets indicators, indexes, operation and maintenance status relations to other assets (e.g. aggregations, dependencies) covering many different thematic aspects from different domains (e.g. economic, environmental, energetic, and social indicators) assets to be modeled and represented in such a way, that allows to fuse these urban information from different domains / disciplines 3 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

4 Challenges in Asset Modeling / Data Provision How should assets be modeled and represented? How to ensure that data from different disciplines can be associated / related to each other? Problem: indicators / parameters from different domains can only be related in a sensible way, if they are referring to the (almost) exact identical entities Thus: different domains need to have a coherent understanding of the urban assets What is an appropriate granularity of urban asset models? Who is defining / giving a reference model? Who can / will provide the data about the urban assets? In a sustainable and reliable way? With full coverage of all (relevant) entities of the urban space? 4 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

5 Spatio-semantic Partitioning as a Reference many urban assets are directly related to physical objects physical objects occupy space in the real world partitioning of the occupied real world space discrete objects criteria for subdivision: thematic classification into different topographic elements like buildings, roads, trees, water bodies etc. spatio-semantic representation of the relevant geoinformation modeling of the city & its constituents objects are categorized spatial properties location, shape, extent different, discrete scale levels (LODs) real world space is 3D semantic 3D city models 5 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

6 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 buildings may be further decomposed into different storeys (and even more detailed into appartements and single rooms) Image: Paul Cote, Harvard Graduate School of Design 6 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

7 Attaching Diverse Information Content The given structuring of the geodata 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 Stable ID value over the lifetime of the object! 7 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

8 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) 8 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

9 9 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

10 Example for a Semantic Building Model 3D-Modell: Stadt Coburg BuildingInstallation (Dormer) BuildingPart Building BuildingPart Building Building surface (WallSurface) 10 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

11 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 11 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

12 (Inter)national Usage / Availability of CityGML Cities / Municipalities e.g. almost all German cities with 3D city models; Rotterdam, Zürich, Geneva, Paris, Marseille, Istanbul, Vancouver, Montreal, Kuala Lumpur, Yokohama, Doha; however, few implementations in the USA (Blacksburg, Boston in preparation) 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 12 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

13 Application Example Strategic Energy Planning 13 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

14 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) Partners: Berlin University of Technology: Innovationszentrum Energie Institut für Geodäsie und Geoinformationstechnik Institut für Energietechnik Institut für Energie- und Automatisierungstechnik Institut für Architektur Institut für Technologie und Management (ITM) Zentrum für Technik und Gesellschaft, TU Berlin Deutsches GeoForschungszentrum Potsdam (GFZ) Vattenfall Europe Berlin AG GASAG AG Berlin Partner GmbH Berliner Senat für Wirtschaft, Technologie und Frauen Bezirksamt Charlottenburg- Wilmersdorf in Berlin 14 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

15 Planning and Decision Support Tool Level Sphere of competence Future projects + Energy-ADE Starter project Political & entrepreneurial level Design & planning level Engineering level Spatial Aggregation Strategic goals, (legal) regulations Cities Urban districts Spatial planning & conceptual design Buildings Quarters Energy efficient components; Energy sources; Distribution networks 15 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

16 Scale Levels of the Energy Atlas City District Quarter / Block Building / Street Appartement Room Generalisation / Aggregation Resolution / Level of Detail 16 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

17 Correlation Consumption Building param s Building data Consumption data Correlation Volume [m³] Floor space [m²] Building type Building usage Year of construction (renovation state) Number of habitants What is the relation of consumption with specific building characteristics? Electricity Water Gas (Remote) Heating 3D City Model Geo Base Data Full coverage of entire cities! Only available for a few households (detailed data only where Smart Meters are installed) 17 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

18 Estimated Heating Energy Consumption Estimated Energy Consumption [kwh/a] 18 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

19 Aggregation Energy Demand Estimation (I) GIS 3D City Model + Geo Base Data + Correlation function Estimation of the individual energy demand for every single building Estimation of the Quarter level District level energy demand City level 19 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

20 Aggregation Energy Demand Estimation (II) GIS 3D City Model + Geo Base Data + Correlation function Estimation of the individual energy demand for every single building 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 District level energy demand City level 20 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

21 Solar Potential Analysis / Solar Atlas Berlin The energy production potentials for photovoltaics and solar thermal heating, the required investments and achievable CO2 reductions have been computed for each of the 550,000 buildings on the level of roof surface segments and were added to the 3D city model 21 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

22 What information do we represent and use? For each of the 550,000 building models in LOD2: Semantic differentiation of surfaces into CityGML RoofSurface, GroundSurface, WallSurface Building usage (e.g. residential, commercial, etc.) Building class (single/multi family house, row house, highrise building etc.) automatically classified Number of storeys above ground derived where not known Wall, roof, ground surface areas [m 2 ], volume [m 3 ] computed Shared wall surface area with adjacent buildings [m 2 ] computed Assignable (usable) floor space [m 2 ] automatically derived Individual (original, not prototypic) textures for each surface Construction year (age classes like , etc.) Status, whether it is a listed / protected historic building Annual heating energy demand (for residential buildings) [kwh] estimated 22 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

23 What information do we represent and use? For each of the 550,000 building models in LOD2: (cont d) Total suitable roof area for photovoltaic installations taking into account orientation, planarity, connectivity, and shadowing [m 2 ] Total suitable roof area for solar thermal installations taking into account orientation, planarity, connectivity, and shadowing [m 2 ] Suitability class for photovoltaic energy production (3 Levels) Suitability class for solar thermal energy production (3 Levels) Maximum achievable electrical power [kw] Achievable annual photovoltaic energy considering the specific meteorological conditions for the location (Berlin) and current generation solar panels [MWh] Achievable annual thermal energy considering the specific meteorological conditions for the location and current generation solar panels [MWh] Investment volume for photovoltaic & solar thermal installations [ ] Potential annual CO2 reduction (according to the current energy mix of Berlin) [tons] 23 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

24 Exploration of Building Energy Parameters 24 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

25 Aggregating Energy Indicators for Districts 25 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

26 What information do we represent and use? City districts as CityGML CityObjectGroups District name District area (2D polygon) All CityObjects within each city district as members Aggregated information from all members of each city district Photovoltaic energy potential [MWh] Solar thermal energy potential [MWh] Heating energy demand [MWh] Any further attributes 26 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

27 Heterogeneous Spatial Aggregations City Supply regions Buildings are aggregated according to city district boundaries, but also according to supply regions (e.g. electricity, remote heating) Along with the spatial aggregation the values of all relevant object attributes / parameters are aggregated, too (e.g. energy consumption, energy production & savings potentials, CO2 share) relating these different aggregation hierarchies a d g b District A c e l k f h j i District B m District C 3 27 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

28 Berlin Geothermal Energy Potential Analysis Each 3D cell is 500m x 500m x thickness of the rock layer at the respective location. Currently represented in CityGML as GenericCityObjects with generic attributes. Estimation of the Berlin underground model and the geothermal potential by J. Sippel, O. Kastner, GeoForschungsZentrum Potsdam 28 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

29 What information do we represent and use? 3D model of the deeper underground (-25m to -3,826m) CityGML GenericCityObjects with LOD2 solid geometries. Resolution: 500m x 500m x variable height. Rich thematic information for every 3D object. Fluid density on top surface [kg/m 3 ] Fluid pressure on top surface [MPa] Mean salinity of fluid on top surface [kg/l] Porosity Energy in the rock [TWh], Rock surface energy density [MWh/m 2 ] Energy in the brine [TWh], Brine surface energy density [MWh/m 2 ] Mean temperature on top surface [K] CityGML CityObjectGroups grouping the different rock layers and their interfacing surfaces Rock layers (in Berlin, from top): Buntsandstein, Keuper, Jurassic, Muschelkalk, Sed_Rotliegend, Zechstein_Salt 29 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

30 Berlin Geothermal Energy Potential Analysis Virtual Drilling / Well Parameters: location & depth Estimation: Sustainably producable geothermal energy; usable energy; Technical efforts 1. Determination of all buildings in an area 2. Summing up the heating energy demand of the buildings 3. Comparison with the amount of usable geothermal energy 4. If more energy is available, increase size of region Estimation of the Berlin underground model and the geothermal potential by J. Sippel, O. Kastner, GeoForschungsZentrum Potsdam 30 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

31 City Model + Multiple Utility Infrastructures 31 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

32 Energy Atlas: Information Fusion Heat Emission Solar Irradiation LOD1 Utility Networks Bild: Hoegner / Stilla, TU München Energy Atlas Geschätzter Energieverbrauch [kwh/a] Heating Energy Requirements Thermal Remote Sensing Solar Potential 32 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

33 Energy Atlas System Design Stakeholder Geoinformatics/ Standards developer Cities (e.g. Berlin) Cities Energy Supplier Housing Companies Energy service provider Citizens Visualization + Reporting - What-if scenarios - Application data acquisition Specific energetic environmental technology issues Consulting 3D City Model + Energy ADE Solar Potential Analyis Acquisition + Conversion + Editing of Cadastre Data Urban Analytics Toolkit Heating Consumption Estimation Development (GIS-Developer /Simulation Experts) City (London) City City GIS Specialists many more modules 33 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

34 Conclusions Semantic 3D City Models ( Urban Information Models) are an appropriate reference model and data platform to attach / link domain specific information across different disciplines Semantic 3D city models 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 (yet?)!! allow for 3D visualizations AND thematic analyses facilitate comprehensive analyses on the urban scale in the fields of e.g. energy assessment, environmental simulation, urban planning accumulate knowledge (including analyses results) Interoperability is key for information integration OGC s CityGML defines the semantic model + exchange format 34 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

35 Some Discussion Points From 3D city modeling to city system modeling explicit representation of dynamics and dependencies (and thus propagation) linking systems (and simulations) from different disciplines bring analytic power closer to citizens (and other stakeholders) open the floor for many more creative minds new / extended formal modeling tools required (coupling of Modelica, SysML, UML, first order logic?) Inclusion of social aspects required Will city models become part of the city infrastructure? (How) can this be brought to / used within cities of the third world? 35 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

36 Spare Slides 36 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

37 Cities and the Climate Protection Goals Reduction of the emission of greenhouse gases like CO 2 Prerequisite for specific measures: knowledge about how much energy of which type for which purpose at what locations is dissipated (and thus demanded); the share in CO2 emission the production of these energy has Total balancing by computing the energy flows in the city / adding of all demands; estimation of CO2 emission by consideration of the corresponding shares of the utilized primary energy In the case of a few big power stations or feed-ins from supra-regional power grids not too difficult to determine But: decentralisation requires spatially resolved data 37 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

38 Problems of the Cities and Energy Providers in the strategic energy planning: simultaneous consideration of multiple aspects / constraints security of energy supply environmental / climate sustainability urban planning regulations building regulations technical / technological aspects acceptance by the citizen require detailed knowledge and consideration of the local situation city development prognoses (population, economy) last but not least: ratio of costs and revenues 38 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

39 Energy Planning Options central vs. decentral energy production e.g. large power stations vs. block heat and power plants exploitation of regenerative & natural energies Solar heat & photovoltaics Wind power Geothermal energy Require detailed knowledge and consideration of the local situation extension, construction, alternative usages of supply / distribution infrastructures measures to increase energy efficiency e.g. building renovation; always affects individual components or buildings in the end influencing of consumer behaviours 39 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management

40 Aggregating Energy Indicators for Districts 40 T. H. Kolbe Semantic 3D City Models for City Lifecycle Management