Designing 3D Semantic Model in LOD4 to Simulate Building Utility Network

Transcription

1 Indian Journal of Science and Technology, Vol 8(16), DOI: /ijst/2015/v8i16/58276, July 2015 ISSN (Print) : ISSN (Online) : Designing 3D Semantic Model in LOD4 to Simulate Building Utility Network Javad Sadidi *, Maryam Talebzadeh, Hani Rezaian and Parviz Zeaiean Firouzabadi Department of Remote Sensing and GIS, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran; Jsadidi@gmail.com, Talebzadeh.m1@gmail.com, Hani.Rezayan@gmail.com, P.zeaiean@gmail.com Abstract In recent years, most of the three dimension city models have been introduced as completely graphic or geometric models, and the semantic and topographic aspects of the models have been neglected. However, in addition to graphic aspects, the semantic three dimension models also comprise of ontological structure including thematic classes, attributes and their mutual relations. City GML (Geographical Markup Language) is an international standard to display and exchange threedimension city models. City GML includes five Different Level Of Details (LOD), and in addition to exterior images of the building, it shows the interior space including the rooms, doors, windows, etc. The aim of the present study is to design a semantic model of the buildings in LOD4 with a precision of 20 square centimeters. LOD4 covers the interior space, and gives a comprehensive and accurate view of both interior and exterior spaces of the building. The gas installations of the buildings are also included in the model as a new and practical design to expand the use of City GML. Such models can be used in intelligent management of the buildings, installations management, and also crisis management in interior spaces of the buildings. Keywords: Application Domain Extension, City GML, 3D Semantic Model, LOD 1. Introduction Today, increasing urbanization, rapid growth of urban areas, and subsequently development of the metropolitan cities around the world are among the important changes occurring in the world. With the increasing urbanization, the number of houses will be more than half of the population of the world. As a matter of fact, there will be about 5 billion houses (United Nations 2008). Therefore, developing techniques to manage the metropolitan cities seem necessary. The semantic 3D models help better comprehend the capacities of spatial features of the cities. The real world has three dimensions, and therefore the man s brain can interpret 3D images more successfully. In recent years, creating 3D city models and geographical information systems are of much interest 1. A 3D environment renders a more precise view of a certain space in comparison with a 2D one 2. Moreover, a 3D map is closer to reality and hence offers an easier analysis. The 3D modelling and mapping are increasingly used by many and diverse users for different reasons. So far, most of the three dimension city models have been introduced as completely graphic or geometric models, and the semantic and topographic aspects of the models have been neglected. Such models are only used in visualization of spaces, and cannot be used in spatial data mining, thematic representations, or analysis. Regarding the mentioned limitations, the widespread usage of such models is not cost-effective. City GML is an open data model based on XML to save, represent and exchange 3D city models 3. In fact, it is an information model which offers a practical model for the language of GML3.1.1 programming 4. The aim of developing City GML is to reach a common definition of basic entities, features, and the relationships *Author for correspondence

2 Designing 3D Semantic Model in LOD4 to Simulate Building Utility Network in a 3D model. Regarding the cost-effectiveness of preserving 3D models, this is an important issue. In August 2008, City GML was announced as the first international standard to show 3D city models by Open Geospatial Consortium (OGC). Over the previous decades, many formats have been introduced to save 3D models. But the special feature of City GML is the capability to show the semantic features of city objects. As a matter of fact, City GML illustrates four different aspects of 3D city models which include: semantic aspect, geometry, topology, and characteristics of the appearance 5. The usage of City GML varies from urban planning, 3D cadaster, crisis management, urban routing, interior design, educational simulations, and security to installations management, etc 3. City GML includes a geometric model and a thematic one. The geometric model offers a fixed and homogenous definition of geometric attributes and topology of objects. The schematic model uses geometric model in different thematic fields such as DTM, places like buildings, bridges, tunnels, vegetation, transportations, etc. Since City GML covers a wide variety of themes (ranging from geometric and topologic aspects to semantic ones) related to city objects, there are logical subcategories of the thematic fields of City GML which reduces the complexity of the model, so that a certain subcategory is used for every application 4. Such subcategories are known as modules in City GML. It includes a core module and eleven expansion modules. The core module encompasses basic components and concepts in City GML data model. In fact, the basis and foundation lies in the core module, and specific thematic fields are located in expansion modules. City GML is capable of supporting any combination of the core and expansion modules. Figure 1 illustrates core and expansion modules. In 3D city modelling, the concept of level-of-details indicates the methods of collecting data for a certain application. City GML supports 5 different level of details. The levels of details are of great use to data analysis and mental visualization. In a dataset in City GML, an object is shown in different levels of details, and simultaneously the analysis and visualization in different degrees of resolution are made possible. The levels of details are of high significance in City GML, and with LODs increasing, more geometric and schematic details of the model are identified 3. The characteristics and precisions of each level of detail are separately shown in the chart below 6. As illustrated in Figure 1, one of the expansion modules in City GML is building module. The building module offers more details about the schematic concept of City GML. This model can clearly show the spatial and thematic aspects of a building 7. The thematic model allows Figure 1. Modularization of City GML (adapted from Kolbe TH, 2008). 2 Indian Journal of Science and Technology

3 Javad Sadidi, Maryam Talebzadeh, Hani Rezaian and Parviz Zeaiean Firouzabadi Table 1. LOD 0-4 of City GML with its accuracy requirements 4 LOD 0 LOD 1 LOD 2 LOD 3 LOD 4 Model Scale Description regional, landscape city, region City districts, projects architectural models(outside), landmark architectural models (interior) Class of accuracy lowest low middle high very high Absolut 3D point accuracy (position /height) Lower than LOD1 5/5 m 2/2m 0.5/0.5m 0.2/0.2m Generalization Maximal generalization (classification of land use) object blocks as generalized features; > 6*6m/3m object as generalized features; > 4*4m/2m object as real features; > 2*2m/1m Constructive elements and openings are represented Building installations representative exterior effects Real object from Roof form/ structure No flat Roof type and orientation real object form real object form Roof overhanging parts - - n.a. n.a. yes City Furniture - Important objects prototype real object form real object form the building to be shown in all five levels of details. In other words, it makes it possible for every kind of building with any degree of complexity (ranging from the buildings comprising of one component to the buildings with complex relationships among different sections) be shown. The building model is the most important component of the semantic City GML model 8. This model can show different components of a building with respect to their geometry, and semantic attributes and properties. In City GML building model, the level of details 0 is a 2.5 dimension level of the earth model which may be provided by an aerial image or a map. In LOD1, the buildings can be observed as blocks having flat roofs. In fact, in this LOD, the structure of the roof is not clear. This level is suitable for modelling wide areas such as a country. The LOD2 includes the structures of the roofs and external installations such as windows, and chimneys. In other words, the structure of roof is different from other levels of the building. In LOD2, the vegetation may be generally specified. LOD3 displays architectural models with details of the roofs, walls, balconies and etc. In this LOD, the structure of the building is depicted with high precision. LOD3 can also indicate vegetation and transportation components. LOD4 is the last and most comprehensive level of details, and includes the internal structure of the buildings such as the rooms, furniture, domestic installations, etc. In effect, by complementing LOD3 through supplying internal structure of the building, LOD4 is reached. Figure 2 displays four level of details for a building model 4. Indian Journal of Science and Technology 3

4 Designing 3D Semantic Model in LOD4 to Simulate Building Utility Network Figure 2. Illustration of the five Levels-of-Detail (LOD) defined by City GML Case Study The case studied in this research is the building of Human Sciences Faculty of Kharazmi University, located in southwest corner of the university. The building is located in 35 42'10" North and 51 25'36" East in Tehran. The building was built in four floors in 1934 based on the Russian Markov s plan. The site occupies approximately 1500 square meters in area and has more than 80 classrooms and rooms. With respect to the fact that the building is over 80 years old, a great importance is attached to 3D models for analyzing, positioning, and decision-making, etc., in the events of accidents or reconstruction of the building. Figure 3 is obtained from Google earth, and displays a view of the mentioned building. Figure 3. Satellite images of the faculty based on the Google Earth. 4 Indian Journal of Science and Technology

5 Javad Sadidi, Maryam Talebzadeh, Hani Rezaian and Parviz Zeaiean Firouzabadi 3. Data and Methodology 3.1 Data To prepare a 3D model of the building, the data below are used: Satellite image of the building (obtained from Google Earth; to develop the building model in LOD0 and LOD1). The building CAD map to design the details inside the building (it is worth mentioning that due to the oldness of the building, the interior structure of the building has undergone some alterations, therefore the building map is updated by a designer through field mapping). Gas pipelines map (due to the absence of map of building installations, the gas pipelines are prepared as manual mapping by a designer). 3.2 Methodology As mentioned earlier, the initial data to do the project is the CAD map of the building. As the first step, to design the 3D model of the building, the lines, dots, and additional polygons were omitted from the CAD map and the CAD file is amended. Then, the map is imported to the Sketch Up software, and the building coordinates are inserted in it. In the next step, using building height data, the 3D model is designed. Then, each component of the building is saved in the relevant layer by City GML plugin, and the features of the building components are added to it. Remove extra layers inautocad CAD map Import Google SketchUp Designing the building model Designing gas pipes Modifying building to its architecture Define readable layers in CityGML Apply texture in layers Validation and editing code in Altova Conversion model to CityGML Final model and display in FZKviewer Figure 4. Workflow of the methodology. Indian Journal of Science and Technology 5

6 Designing 3D Semantic Model in LOD4 to Simulate Building Utility Network Figure 5. The faculty building in different LODs. Figure 5, depicts the building model in all four level of details. Since the model is used in events of accidents and for crisis management, its precision is high, and after being designed by field observations, minor alterations have been made. The next step after designing the building model is designing the gas pipelines. As the building is old, and the building installations map is not accessible, the gas pipelines map is drawn through field observations. The 3D model of the pipes is designed in Sketch up Software, and its features (such as material, the pipe diameter, etc.) are added to the mentioned layer by City GML plugin. Figure 6 displays a part of the needed code for gas pipes. Eventually, the XML output is studied and corrected in Altova XML spy. The most important point in correction and validation of GML code is to determine the coordinates system. In this project, the WGS84 Coordinate System is used. Figure 7 displays a part of GML code related to coordinate system. Figure 6. GML code used for a gas pipe. 6 Indian Journal of Science and Technology

7 Javad Sadidi, Maryam Talebzadeh, Hani Rezaian and Parviz Zeaiean Firouzabadi <City gml:id= Capital City > <Name>Tehran</Name> <gml:position> <gml:point srsname= WGS84 > <gml:coordinates> ,530000</ gml:coordinates> </gml:point> </gml:position> <IsCapitalOf xlink:href= #T1 /> </City> <Country gml:id= T1 > <Name>IRAN</Name> <Continent>Asia</Continent> <Region>Southern Asia</Region> <Capital xlink:href= Capital City /> </Country> Figure 9. Output the building model in Sketch up software. Figure 7. The output file in the present paper is the building model in LOD 4 under City GML standard. The building model is written in lines, and the size of the output file is 11 MB. Figure 8. GML code related to coordinate system. A view of the gas pipe in the pantry. 4. Discussion and Further Works With increase of buildings and constructions, the significance of spatial and semantic information management has also increased. In the present paper, the 3D semantic model of the building is designed in LOD4 based on City GML. In addition to the exterior space, the interior space (classrooms, stairs, doors, windows, elevators, etc.) was also designed with the precision of 20 square centimeters. Moreover, the building structure can be observed in the model. In many countries including Iran, the building installations maps are in paper and in the form of 2 dimension maps. Using and preserving such maps is difficult and time-consuming in events of accidents. In the current study, for the first time, the gas installations of the building are presented as a novel and practical plan to expand the domain of City GML application in LOD4. The main purpose of the study is to investigate different aspects of 3D modelling, and the advantages and applications of City GML as a new standard. The second purpose is to expand the domain of City GML application in order to prepare a 3D map of building installations for a more qualified management of the buildings, and prevention from casualties and financial losses in the events of accidents or other problems. Practically, preparing such 3D semantic models help design intelligent buildings. It is worth mentioning City GML is a new standard which requires to be developed in order to be used in different Indian Journal of Science and Technology 7

8 Designing 3D Semantic Model in LOD4 to Simulate Building Utility Network applications. In the current study, the City GML standard is used as a semantic model for the building and its internal components, and also for a 3D information exchange. Moreover, the gas installations are introduced as a new plan to expand the usage of City GML. In the future, research can be done on WEB City GML, and the exact location of gas-pipe leaks can be determined by placing sensors. 5. References 1. Mao B. Visualization and generalization of 3D City Models [PhD thesis]. Stokholm: Royal Institute of Technology (KTH); Şengül A. Extracting semantic building models from aerial stereo images and conversion to City GML [PhD dissertation]. Istanbul: Istanbul Technical University; Kolbe TH. 3D geo-information sciences. Part I, Representing and Exchanging 3D City Models with City GML. Berlin: Springer Berlin Heidelberg; 2009 [Cited 2014 Oct 26]. Available from: %2F _2 4. Gorger G. Kolbe T, Czerwinski A, Nagel C. Open GIS City Geography Markup Language (City GML) Encoding Standard. Germany: Open Geospatial Consortium; 2012 [cited 2014 Nov 10]. Available from: 5. Koukofikis A. Evaluation of validation and healing approaches for 3D city models & Integration of a new healing feature into CityServer3D [MSc Thesis]. Stuttgart: College of Engineering Stuttgart; Mao B, Ban Y, Harrie L. A multiple representation data structure for dynamic visualization of generalized 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing. 2011; 66(2): Mao B, Ban Y, Harrie L. A Framework for generalization of 3D city models based on CityGML and X3D. ISPRS Workshop on Quality, Scale and Analysis Aspects on Urban City Models; Sanchez CAL. Estimation of Electric Energy Demand using 3D City Models [MSc Thesis]. Berlin: Institute for Geodesy and Geo information Science Faculty VI Planning Building Environment; Nouvel R, Schulte C, Eicker U, Pietruschka D, Coors V. City GML-based 3D City Model for energy diagnostics and urban energy policy support. Proceedings of IBPSA World; Canada: Sengupt S. GIS-based Smart Campus System using 3D Modeling. Proceedings of Geospatial World Forum; Hyderabad, India Jan. p Zlatanova S, Stoter J, Isikdag U. Standards for Exchange and Storage of 3D Information: Challenges and Opportunities for Emergency Response. Proceedings of the 4th International Conference on Cartography and GIS; Albena, Bulgaria Jun. p Fan H, Meng L. A three-step approach of simplifying 3D buildings modeled by City GML. International Journal of Geographical Information Science. 2012; 26(6): Kahraman I, Karas IR, Rahman AA. Developing Web- Based 3D Campus Information System. Proceedings of the 10th International Symposium of Geoinformation and ISPRS; Selangor, Malaysia. 3D Geo-Informatics Research Group, KARABUK; 2011 Sep. p El-Mekawy M, Ostman A, Hijazi I. A unified building model for 3D urban GIS. ISPRS International Journal of Geo-Information. 2012; 1(2): Baig SU, Rahman AA. A three-step strategy for generalization of 3D building models based on CityGML specifications. Geo Journal. 2013; 78(6): Kolbe TH, Groger G, Plumer L. CityGML: Interoperable Access to 3D City Models. Geo-information for disaster management. Berlin: Springer Berlin Heidelberg; p [Cited 2014 Jan 28]. Available from: springer.com/chapter/ %2f _ Willmes C, Baaser U, Volland K, Bareth G. Internet based distribution and visualization of a 3D model of the University of Cologne Campus. 3rd ISDE Digital Earth; Mao B, Ban Y, Harrie L. A Framework for generalization of 3D city models based on CityGML and X3D. ISPRS Workshop on Quality, Scale and Analysis Aspects on Urban City Models; Gesquiere G, Manin A. 3D visualization of urban data based on City GML with WebGL. IJ3DIM. 2012; 1(3): Groger G, Plumer L. City GML Interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing. 2012; 71: Czerwinski A, Sandmann S, Stocker-Meier E, Plumer L. Sustainable SDI for EU noise mapping in NRW best practice for INSPIRE. International Journal for Spatial Data Infrastructure Research. 2007; 2(1): Hijazi I. Integrated management of indoor and outdoor utilities by utilizing BIM and 3DGIS [PhD Thesis]. Germany: University of Osnabruck; Goetz M, Zipf A. Towards defining a framework for the automatic derivation of 3D City GML models from volunteered geographic information. IJ3DIM. 2012; 1(2): Lowner MO, Benner J, Groger G, Hafele KH. New Concepts for Structuring 3D City Models an Extended Level of Detail Concept for CityGML Buildings. Proceedings of the 8 Indian Journal of Science and Technology

9 Javad Sadidi, Maryam Talebzadeh, Hani Rezaian and Parviz Zeaiean Firouzabadi 13th International Conference; 2013; Ho Chi Minh City, Vietnam. Springer Berlin Heidelberg; p Kim YJ, Kang HY, Lee J. Development of Indoor Spatial Data Model Using City GML ADE. Journal of Korea Spatial Information Society. 2013; 21(2): Malinverni E, Tassetti AN. GIS-based Smart Cartography Using 3D Modeling. ISPRS-International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 2013; 1(2): Indian Journal of Science and Technology 9