3-D URBAN MAPPING FOR A HYBRID GIS

Festschrift Prof. Dr. Ing. Heinrich Ebner zum 60. Geburtstag, München, 1999, S. 97-107 3-D URBAN MAPPING FOR A HYBRID GIS Armin Gruen Xinhua Wang Institute of Geodesy and Photogrammetry Swiss Federal Institute of Technology (ETH) Zürich ETH Hönggerberg, CH-8093 Zürich, Switzerland Fax: 41-1-6331101 E-mail: agruen@geod.ethz.ch wang@geod.ethz.ch Das ist eine von den alten Sünden, sie meinen Rechnen, das sei Erfinden. KEY WORDS: CyberCity Modeler, Relational Data Model, Hybrid GIS ABSTRACT Goethe; Mephisto, Faust I CyberCity-Modeler (CC-Modeler) is a methodoloy and a software for the automatic generation of the topology of an unstructured 3-D point cloud. It has been developed in order to generate structured data for city modelling from photogrammetrically measured points. While CC-Modeler has primarily been designed to generate buildings, it can be applied to other objects of the real world consisting of planar faces as well, as long as these objects obey the functional constraints defined for buildings. A detailled technical description of CC-Modeler has been given in Gruen, Wang, 1998. This paper aims at reporting about the performance and progress of CC-Modeler, and will also describe the status of the development of our spatial information system CC-SIS, which is specially designed for the handling of 3-D city data, and the integration of raster images and vector data in terms of a hybrid GIS. 1. INTRODUCTION Urban 3-D mapping became an important issue in the recent past due to the increasing demands for a realistic representation of the real world. There are usually three types of data sets involved: Digital Terrain Model (DTM), objects like buildings, roads, waterways, etc. and original images or desired orthoimages. The DTM is the most standardized data type in a 3- D city model, and it usually is represented as regular grid, Triangular Irregular Network (TIN) or hybrid. Many investigations have been performed concerning the implementation of those representations (Fritsch, Pfannenstein, 1992). Considering the complexity of the terrain structure in most city areas, the TIN structure is preferred as a more suitable structure for DTM representation. As to the representation of objects, several methods for 3-D object description have been investigated. Those models can be grossly subdivided into Wire- Frame, B-Rep, VBR, Cell-Decomposition, FBR, CSG, E-complex, FDS [Molenaar 1992, Rikkers, Molenaar, 1994]. Breunig, 1996 has discussed their suitability and insufficiency for 3-D GIS by comparing the different 3-D representations according to the following criteria: domain, validity (geometric and technical), non-ambiguity and unambiguity, close operation, efficiency of geometric algorithm, accuracy, need for storage. In most applications, images are used as realistic texture data in order to present details which are not modelled in the vector data set and give information about material properties. Therefore, for visualization purposes, images may even compensate for lack of modelling of fine details. From a data structure point of view, images are expressed as 2-D raster data, which can be stored or manipulated as a special layer in our 3-D system. This situation led us to develop an appropriate data model, which should not only represent the geometrical information, but also implicitly or explicitly describe the topological relationship. In the case of texture mapping, it also must have the ability to manipulate raster images. At the Institute of Geodesy and Photogrammetry, ETH Zürich we are involved in an interdisciplinary project "Integration of Raster Images and 3-D Objects into Geodatabases". Two important research topics are treated by our group: (a) the generation of the topology of 3-D objects by using photogrammetric tools; (b) the investigation of the data model and the development of a prototype system to manage the vector data and raster images

based on the relational database technology. The detailled technical description of the former problem is addressed in Gruen, Wang, 1998 with our CC- Modeler (CyberCity Modeler). Here, we will present a technology for management of data, which is implemented in our CC-SIS (CyberCity Spatial Information System). This paper is arranged in the following manner: In section 2 we will briefly explain the procedure of CC-Modeler. The data structure will be presented in section 3. In section 4 the configuration and the issue of implemen-tation based on relational database technology are addre-ssed. Finally, an application prototype is presented. 2. CC-MODELER Photogrammetry is an appropriate tool to provide information about man-made objects, vegetation cover and the like. Recently, many approaches for automated and semi-automated extraction of buildings and roads from aerial images have been proposed (Gruen et al., 1997). Due to the complexity of natural scenes and the lack of performance of image understanding algorithms, the fully automated methods cannot guarantee results stable and reliable enough for practical use. Therefore, we have developed a semi-automated approach, which gives the human operator strong computational support in order to generate 3-D city models from aerial images efficiently. With CC-Modeler (CyberCity Modeler) we present a new method for fitting planar structures to measured sets of point clouds. While this topology generator has been originally designed to model buildings, it can also be used for other objects, which may be approximated by polyhedron surfaces. We have used it so far for roads, rivers, parking lots, ships, etc. CC-Modeler is a generic topology generator. 2.1 General data flow and data processing To generate 3-D descriptions of man-made objects from aerial images involves two major components: photogrammetric measurements and structuring. In CC-Modeler, the feature identification and measurement is implemented in manual mode, on an Analytical Plotter or a Digital Station. During the data acquisition, 3-D points belonging to a single object should be coded into two different types according to their functionality and structure: boundary points and interior points (see Figure 1). Boundary points must be measured in a particular order, either clockwise or counter-clockwise. Interior points can be measured in an arbitrary sequence. Since the human operator is responsible for the interpretation and measurement, it is possible to acquire any level of object detail for buildings, roads, waterways and other objects. With this technique, hundreds of objects can be measured in one day. CC-Modeler is an automatic topology generator for 3-D objects. The main components of the system are shown in Figure 2. A more comprehensive description of the algorithmic details is given in Gruen, Wang, 1998. The first obligatory step is preprocessing, which includes the checking of the measurement order of the boundary points (BP), detection of redundant points, and determination of the possible groups of faces, based on sets of adjacent (BP) point pairs. P 7 P 5 P 6 P 9 P 10 P 8 P 7 P 8 P 13 P 12 P 5 P 6 P 9 P 10 P 13 P 12 P 2 P 1 P 2 P1 P 11 P 1,, P 10 : Boundary points (BP) P 11,, P 13 : Interior points (IP) P 11 P 4 P 3 P 4 P 3 (a) (b) Figure 1: (a) Point definition in CC-Modeler (b) Related graph

The next step is to build the face model of the 3-D object, i.e. to determine how many faces the 3-D object has, which points define an exact face and the spatial relations between the faces. This is implemented through a Consistent Labeling algorithm by probabilistic relaxation operations, in which two procedures are involved, the initial probability determination and the relaxation processing. The result of Consistent Labeling is the Analytical plotter Digital station (I) Point clouds Preprocessing Initial probabilities Relaxation processing (I) VRML Face definition & L.S adjustment Edit (I) AutoLisp V3D Interface DXF Triangulation IV DTM Vector models Texture mapping CosmerPlayer AutoCAD MicroStation Polytrim ArcView Inventor Visualization Figure 2: The data flow of CC-Modeler (I) ---- Interactive functions Figure 3: An overview of the CC-Modeler userinterface

face definition for every face. Then, least squares adjustment is performed for all faces simultaneously, fitting the individual faces in an optimal way to the measured points and considering the fact that individual points are usually members of more than one face. This adjustment is amended by observation equations that model orthogonality constraints of pairs of straight lines between boundary points. Finally, a vector description of 3-D objects is obtained, which is represented in a selfdeveloped data structure (V3D). For the purpose of visualization, the CC-Modeler can also triangulate the faces to a TIN structure. Although the procedure of topology generation is automated, human intervention and interaction is also available, as outlined in Figure 2. V3D is a self-developed vector data structure, which builds the facet model of objects. In addition, CC- Modeler has the ability to map images onto a 3-D object or DTM in order to create a more natural scene. The texture, taken from the original images, is attached to an exact face as a special attribute. With the help of interfaces, one can conveniently translate the V3D data structure into different data files, such as DXF, IV, and AutoLisp, VRML which are readable by AutoCAD, MicroStation, Polytrim, ArcView, Inventor and CosmoPlayer. For more details about the V3D data structure we refer to Section 3. CC-Modeler has been successfully implemented on workstations (Sun SPARC) under X-Windows and OSF/Motif. Figure 3 is an overview of the CC- Modeler userinterface. One can optionally set CC- Modeler to work in the automatic or the interactive mode. When working in the interactive mode, one can edit or modify 3-D objects with the help of 2-D or 3-D views, if any mistakes occur due to inadequate point measurements. With the 2-D view, CC-Modeler will project the vector data of 3- D objects onto the original image (see Figure 3) to help check the results. In the 3-D mode, a 3-D vector model of the object will be shown to monitor the procedure. 2.2 Results of several projects and visualization CC-Modeler has been tested in several pilot projects, from which the statistics of those data sets are presented in Table 1. "Structured automatically" refers to the number of roof units that CC-Modeler builds successfully with fully automatic processing, and "structured interactively" refers to the number of roof units that needed to be manually modified in some faces. Obviously, the success rate of CC- Modeler's automatic processing is better than 95%, and almost all roof units can be constructed by using the convenient editing tools. The main factor determining the performance is the degree of familiarity of the human operator with the concept of automated reconstruction. Investigating the procedure of CC-Modeler processing, we find that the reason of CC-Modeler failing to process an object is that the measured point cloud was incorrectly coded, which results in the fact that the labeling results do not meet the topological constraint conditions which are implicitly included in the Consistent Labeling algorithm. CC-Modeler may result in some inadequate face definitions for some objects (about 4% in Tab.1), which need interactive modification with the help of editing tools. The main reasons for that are measurement errors and ambiguities in topological relations (under certain conditions there may exist more than one valid solution). With a person well familiar with CC-Modeler, over 500 roof units can be generated per day. The CPU times (4 roof units per second, based on Sun SPARC platforms) indicate that the procedure of structuring can be executed on-line, while the operator is measuring, and in real-time. Table 1: CC-Modeler statistics of various projects Project Total No. of Roof units Structured Automatically Structured interactively Failures CPU time (sec) Zürich Downtown 4729 4487 240 2 1493 Zürich Stadelhofen 553 534 19 0 184 Zürich Oerlikon 7253 6971 279 3 2089 Melbourne University 1136 1104 32 0 313 ETH Hoenggerberg 172 170 2 0 53 Dietikon 298 290 8 0 56 Regensdorf 925 894 30 1 165 Firenze Center 1544 1509 33 2 504 Giessen 4157 4122 35 0 1296 Total 20767 20081 678 8 6153

Figure 4: City model Zürich Oerlikon Figure 5: Data set Zürich Downtown Figure 6: Data set Giessen

Figure 7 : Tokyo Downtown with partially mapped texture For the visualization and animation of the data sets we use various software: AutoCAD, MicroStation, Inventor, and Polytrim. Figures 4 to 6 show views of the city models "Zürich Oerlikon, Zürich Downtown" and Giessen, including buildings, rivers, trees and DTM. For photorealistic rendering we combine the vector data of the buildings and the DTM with image raster data. The raster images are taken from aerial images. Figures 7 and 8 show the result of mapping image data onto the DTM and some walls and roofs. Figure 8 shows in addition streets in 3-D representation and the integration of a planned building. CC-Modeler can also map digital images, taken with still video cameras, onto 3-D faces, such as the walls of buildings. Figure 8: Zürich, ETH campus Hoenggerberg with mapped texture and planned building (at the upper right)

3. DATA STRUCTURE V3D is a hybrid data structure. It not only models 3- D objects, but also combines raster images and attribute information for each object. The objects are grouped into four different geometric object types: Point Objects: zero-dimensional objects which have a position but no spatial extension. Line Objects: one-dimensional objects with length as the only measurable spatial extension, which means that Line Objects are built up of connected line segments. Surface Objects: two-dimensional or two and half-dimensional objects with area and perimeter as measurable spatial extension, which are composed of facet patches. Body Objects: three-dimensional objects with volume and surface area as measurable spatial extension which are bordered by facets. In V3D, each special object is identified by Type Identifier Code (TIC), referred to as PIC, LIC, SIC and Class 1 Class 2 Class 3 Class 4 Class 5 Point Line Object Surface Body Object DTM Attributes Attribute Attributes Attributes Attributes Point Edge Facet Entity Image (Texture) Figure 9: The logical data structure of V3D BIC, respectively. Two data sets are attached to each object type: thematic data and geometric data. The image data can be attached to the surface object, body object and DTM object. In fact, the thematic data attributes are built up in a separate data table. It is linked to the object type with a related class label. The definition of the thematic data is user-dependent. The geometric data set contains the geometric information of 3-D objects, i.e. the information of position, shape, size, structure definition, and image index. The diagram in Figure 9 shows the logical data structure. For the four object types, four geometrical elements are designed, i.e. Point, Edge, Facet and Entity. Point is the basic geometric element in this diagram. The Point can present a point object. It also can be the begin or end point of an Edge. The Edge is a line segment, which is an ordered connection between two points: begin point and end point. Further, it can be a straight part of a line object or lie on a facet. The Facet is the intermediate geometrical element. It is completely described by the ordered edges that define the border of the facet. One or more facets can be related to a surface object or Entity geometrical element. Moreover, Facet is related to an image patch. Entity is the highest level geometrical element, and it can carry shape information. An entity is completely defined by its bordering facets. Image data and thematic data are two special data sets, which are built up in two separated data tables. Each facet is related to an image patch through a corresponding link. Once the attribute table is attached and the TIC is labelled, a geometrical element becomes an object type. The DTM is treated as a special data type,

which is described by a series of facets. Obviously, the topological relationships between geometrical elements are implicitly defined by the data structure. A point object is presented by a distinct Point element. The line object is described by ordered Edges. The surface object is described by the Facet with the information of image patches. Similarly, the body object is described by Entity that are defined by the facets. Thus, the topological relationships between Point and Edge, Edge and Facet, Facet and Entity are registered by the links between the geometrical elements. 4. IMPLEMENTATION IN A RELATIONAL DATABASE In a relational database the most common object to be manipulated is the relation table. Other objects such as index, views, sequence, synonyms and data dictionary are usually used for query and data access. "Table" is the basic storage structure, which is a two-dimensional matrix consisting of columns and rows of data elements. Each row in a table contains the information needed to describe one instance of the entity, each column represents an attribute of the entity. The data model shown in Figure 9 is a relational model, which can be implemented by relational data base technology. Figure 10 shows the relational model of the V3D data structure. Each object type is defined as a table, shown as the upper row. A point type table includes three terms. The Point Identification Code (PIC) is an identifica- Point type Line type Surface type Body type DTM type PIC AID NP LIC AID SIC AID BIC AID DID AID Pd1 pole P1 Ld1 path Sd1 Water Bd1 House Dd1 DTM1 Pd2 tree P2 Ld2 Pipe Sd2 Road Bd2 Tower Dd2 DTM2 LIC-NIL NIL-SID Facet-Entity-Image DID-SID-Image NIL LIC NIL SID SID SIC BIC ImgId SID DID ImgId L1 Ld1 L1 SI1 SI1 Sd1 Bd1 Img1 SI1 Dd1 Imgi L2 Ld2 L2 SI2 SI2 Sd1 Bd1 Img2 SI2 Dd1 Imgi L3 Ldi L3 SI3 Sii Sd2 Bd2 Imgi SI3 Dd2 Imgj NP X Y Z NIL BP EP ImgId name type data Pams P1 X1 Y1 Z1 L1 P1 P2 Img1 N1 Tiff P2 X2 Y2 Z2 L2 P2 P3 Img2 N2 Gif P3 X3 Y3 Z3 L3 Pi Pi+1 Img3 N3 RGB Point Edge Image Figure 10: The relational model of the V3D data structure tion code for a point type object. The Attribute Identification (AID) is coded to relate an attribute table. Different types of objects may have different attribute tables. For example, the attribute tables of "tree" may have different thematic definitions than "pole". The Name of Point (NP) is the identification of a geometric point, which is used to relate it with a distinct element in a point geometric element table. The Point table is the most basic geometrical element table, which defines the coordinate position of the geometrical points. The Line type table has similar content as the Point type table. The difference is that a line type object is identified by the Line Identification Code (LIC) which is not directly linked with the geometric element table Edge, but linked with an intermediate relational table LIC-NIL and then indexed to the Edge table. The Edge table defines the geometrical

element Edge, in which each edge (NIL) is described by the beginning point (BP) and the end point(ep). The Surface type table and Body type table have similar terms as the Line type table. For each type of object a distinct identification code (SIC or BIC) is labelled. Both SIC and BIC are linked with a merging geometrical element table, Facet-Entity-Image, in which the topological relationships between Facet and Surface, Facet and Entity, Facet and Image are defined. Facet-Entity- Image table has two links: one is related to the Image table; the other is related to the NIL-SID table. Image table is a basic table, which describes all attributes of images, such as the image name, format, pixels, camera parameters, orientation parameters etc. The NIL-SID table is another intermediate table, which defines the corresponding relationships between Facet and Edge. Its NIL column is related to the Edge table. The DTM is treated as a special class, which is related to the NIL-SID table through an intermediate table DID- SID-Image. Based on the relational structure shown on the diagram in Figure 10, the query of a geometrical description of a distinct object type is easily realized. For example, the query "Select the geometrical description of an object with the identification code BIC = 202", will first index all Facet identification in the Facet-Entity-Image table by its BIC, and then get all edge name identification (NIL), finally index the position information of structure points with the help of Edge table and Point table. The queries of topological relationships are divided into two types: relationships between the geometrical elements of an object and those between objects themselves. The relationships between the geometrical elements are implicitly defined in the above data structure. Though the internal topology is not directly supplied, users can flexibly deduce the relationships, such as joint, adjacency, left or right, etc. The queries of topological relationships between objects are not considered in the above data structure because they are application-dependent. CC-SIS Input/output Edit View Image Data Geo-query Database Output DXF Iv V3D Autolisp 3D data Images CC-Modeler DXF Input 2D view Wireframe Shading Texture View port Zoom View type 3D view Int. orientation Image attributes Layer Add Delete Lock Unlock Color Attributes input Geometry Topology Database link SQL query Point Line Area Buffer Adjacent Link In/out ORACLE Figure 11: The function units of CC-SIS 5. PROTOTYPE SYSTEM Based on the above data model and structure, a special information system, CC-SIS, has been successfully developed and implemented on a workstation (Sun SPARC) under X-Windows, OSF/Motif, OpenGL as well as ORACLE database. Although its main purpose is scientific investigations, it can also be used in applications like photorealistc representation with possibilities for navigation through the 3-D city model, creation, storage, analysis and query of a city object. In combination with our topology generator CC- Modeler, it builds up a system for geometrical information generation, storage and manipulation.

Figure 12: Artificial wall texture produced with the Image function Figure 13: The geometrical query of CC-SIS There are currently seven function units in CC-SIS shown in the Figure 11. CC-SIS can directly read the data file generated by CC-Modeler and output results in the format DXF, Autolisp, Iventor and V3D. The Edit function is used for graphic editing, which is to be developed in the future. The View function supplies the tools for 2-D or 3-D viewing, such as dynamically selecting a view port, zooming, etc. Further, three types of rendering are available, wireframe, shading and texture mapping. The Image function supplies the tools for interior orientation of the images in order to map natural texture from images. Also, artificial texture can be mapped. An example of this function is shown in Figure 12. The manipulations of data are supplied by the Data function. It includes two sub-modules: one is used

for the operation on layers (objects are defined as different layers in CC-SIS, such as building, DTM, waterway, pathway, tree, etc.); the other is to input the attributes for the selected object. The Geo-query function includes two tools: geometry query and topology query. The former is used to query the separated object by the point, line or entity selection; the latter is employed to query topological relationships between different objects. Figure 13 shows the geometric query of CC-SIS. The user can mark an object (e.g. building) with a cursor. This triggers and displays the corresponding attributes and geometrical/topological information. The operations on a database are defined in the Database function, including database link and SQL-query. SQL-query is a sub-menu, in which standard SQL queries are supplied. 6. CONCLUSIONS CC-Modeler is a powerful data acquisition tool for the generation of 3-D city models. Our experiments show, that it is flexible, robust and accurate. In several projects we have achieved a success rate of better than 95% in fully automated structuring. Remaining problems are indicated and can be solved interactively. We have developed our own data structure V3D with interfaces to a variety of CAD and visualization packages. CC-Modeler cannot only reconstruct multiple kinds of 3-D and 2- D objects such as buildings, waterways, roads, trees, DTM, etc., but also map images onto these objects. This can be combined with data from general land use, communication systems, utilities, property and administrative boundaries, etc. or even objects under planning to generate a complete 3-D city model. Given an efficient method for 3-D data acquisition, the generation of a powerful 3-D spatial information system becomes even more mandatory. Our prototype system CC-SIS (CyberCity Spatial Information System) has proven to represent a suitable concept which is worth developing further Based on our proprietary V3D vector data structure, a relational database has been created. The data to be operated can be logically separated into vector data, images and thematic information. In this paper the focus is on the geometrical part of the database (vector data and images). Our pilot applications show that V3D is a suitable structure for the representation of 3-D objects, images and thematic data. It is possible to answer most of the questions about topology, position and shape of objects by means of geometric or SQL queries. CC-SIS has been proven to be used for the following applications: Photorealistic representation with possibilities for navigation through the 3-D city model Abilities to create, store, design, analyse and query city objects 7. ACKNOWLEDGMENTS: The data set "Tokyo Downtown" was produced by Mr. Tsukasa Sampei with CC-Modeler. We appreciate his help very much. REFERENCES 1. Breunig, M., 1996, Integration of spatial information for geo-information systems, Springer Corp., Berlin. 2. Fritsch, D., Pfannenstein, A., 1992, Integration of DTM data structures into GIS data models, In: Int. Archives of Photogrammetry and Remote Sensing, Washington, Vol. XXIX, B3, pp. 497-503. 3.Gruen, A., Wang, X., 1998, CC-Modeler: A topology generator for 3-D city models, ISPRS Journal of Photogrammetry & Remote Sensing, Vol.53, No.5, October, pp.286-295. 4. Gruen, A., Baltsavias, E., Henricsson, O., (eds.), 1997, Automated extraction of man-made objects from aerial and space images(ii), Proceedings of the Monte Verita Workshop, May 1997, Birkhauser Verlag, Basel. 5. Molenaar, M., 1992, A topology for 3D vector maps, ITC Journal, No. 1, pp. 25-33. 6. Rikkers, R., Molenaar, M., 1994, A query oriented implementation of a topologic data structure for 3-dimensional vector maps, International Journal of Geographical Information Systems, Vol. 8, No. 3, pp. 243-260.