More 3D GIS academic papers

Köninger A, Bartel S. 3d-Gis for Urban Purposes. GeoInformatica [serial online]. March 1998;2(1):79. Available from: Academic Search Premier, Ipswich, MA. Accessed March 28, 2009.

Abstract:
New developments in urban planning, especially in environmentally oriented analysis including noise, air pollution, urban climate etc., call for new demands on authorities and planners. Due to the increasing availability of informations systems and of 3D-data, planners and municipalities emphasize modeling the urban space in three dimensions. While the visualization aspect is often and detailed considered, only a few investigations about interactive aspects on urban planning are available. In this paper we present a framework for a 3D-urban-GIS. This includesvconceptual aspects and a ®rst outline and implementation of an application prototype. For this representation, new scopes have to be considered from data acquisition to modeling and to storage. First, the urban object space is classifed in an hierarchical 3D object structure. In accordance to different planning levels (i.e., levels-of-detail), several data acquisition methods are fused to obtain 3D datasets. The results show that a context speci®c methodology has to be de®ned. This includes planning aspects that are traditionally not available in GIS. Based on test sites in Rostock and Stuttgart, a 3D urban-GIS prototype is in development, joining aspects of a 3D-visualization interface and a database for 3D objects.

Click here to download this PDF

Lee J, Kwan M. A combinatorial data model for representing topological relations among 3D geographical features in micro‐spatial environments. International Journal of Geographical Information Science [serial online]. November 2005;19(10):1039-1056. Available from: Academic Search Premier, Ipswich, MA. Accessed March 28, 2009.

Abstract:

This research is motivated by the need for 3D GIS data models that allow for 3D spatial query, analysis and visualization of the subunits and internal network structure of ‘micro‐spatial environments’ (the 3D spatial structure within buildings). It explores a new way of representing the topological relationships among 3D geographical features such as buildings and their internal partitions or subunits. The 3D topological data model is called the combinatorial data model (CDM). It is a logical data model that simplifies and abstracts the complex topological relationships among 3D features through a hierarchical network structure called the node‐relation structure (NRS). This logical network structure is abstracted by using the property of Poincaré duality. It is modelled and presented in the paper using graph‐theoretic formalisms. The model was implemented with real data for evaluating its effectiveness for performing 3D spatial queries and visualization.

Click here to download this PDF

Ellul C, Haklay M. Requirements for Topology in 3D GIS. Transactions in GIS [serial online]. March 2006;10(2):157-175. Available from: Academic Search Premier, Ipswich, MA. Accessed March 28, 2009.

Abstract:

Topology and its various benefits are well understood within the context of 2D Geographical Information Systems. However, requirements in three-dimensional (3D) applications have yet to be defined, with factors such as lack of users’ familiarity with the potential of such systems impeding this process. In this paper, we identify and review a number of requirements for topology in 3D applications. The review utilises existing topological frameworks and data models as a starting point. Three key areas were studied for the purposes of requirements identification, namely existing 2D topological systems, requirements for visualisation in 3D and requirements for 3D analysis supported by topology. This was followed by analysis of application areas such as earth sciences and urban modelling which are traditionally associated with GIS, as well as others including medical, biological and chemical science. Requirements for topological functionality in 3D were then grouped and categorised. The paper concludes by suggesting that these requirements can be used as a basis for the implementation of topology in 3D. It is the aim of this review to serve as a focus for further discussion and identification of additional applications that would benefit from 3D topology.

Click here to download this PDF

Lee J. A Spatial Access-Oriented Implementation of a 3-D GIS Topological Data Model for Urban Entities. GeoInformatica [serial online]. September 2004;8(3):237-264. Available from: Academic Search Premier, Ipswich, MA. Accessed March 28, 2009.

Abstract:

3-D analysis in GIS is still one of the most challenging topics for research. With the goal being to model possible movement within the built environment, this paper, therefore, proposes a new approach to handling connectivity relationships among 3-D objects in urban environments in order to implement spatial access analyses in 3-D space. To achieve this goal, this paper introduces a 3-D network data model called the geometric network model (GNM), which has been developed by transforming the combinatorial data model (CDM), representing a connectivity relationship among 3-D objects using a dual graph. For the transformation, this paper presents (1) an O(n²) algorithm for computing a straight medial axis transformation (MAT), (2) the processes for transforming phenomena from 3-D CDM to 3-D GNM, and (3) spatial access algorithms for the 3-D geometric network based upon the Dijkstra algorithm. Using the reconstructed geometric network generated from the transformations, spatial queries based upon the complex connectivity relationships between 3-D urban entities are implemented using Dijkstra algorithm. Finally, the paper presents the results of an experimental implementation of a 3-D network data model (GNM) using GIS data of an area in downtown Columbus, Ohio.

Click here to download this PDF

Information on Existing 3D GIS systems and projects

Oracle Spatial, Locator, and Location Based Systems

This Oracle technology is a set of database and GIS technologies that incorporate things like geocoding, location based services, and 3D GIS in an all encompassing system. It provides a platform that supports a wide range of applications—from automated mapping/facilities management and geographic information systems (GIS), to wireless location services and location-enabled Business Intelligence.

A system that makes use of this technology can be found at: http://www.abacogroup.com/eng/news/newsletter/newsletter21.htm

This system is called "Real-time web 3D Navigation of the Oracle Spatial 11G 3D Data Types". This product is very impressive and allows an real time and continuous navigation through Oracle Spatial data types.

Data Acquisition and Manual Preprocessing

The following describes the process used to convert raw CAD files to the format used for graph construction, visualization, and other analysis tasks.


Data acquisition begins by collecting all CAD building files for the building that will be incorporated into our database. For most recent buildings, these are in the form of “.dwg” CAD drawing files and are easily read by ArcGIS software. In order for the files to be inserted into our PostGIS database, they must first undergo some manual preprocessing to ensure homogeneity as well as eliminate any errors or data that is not needed.

Since these CAD files contain no spatial reference information, they must first be geo-reference to a base map so they line up with our other datasets. For this process, I use the Spatial Adjustment tools in ArcGIS to define control points on the base map and point them to the same areas on the CAD building file. These are usually corners of the building or other defining features that will allow the algorithm to line up the data correctly with minimal distortions. Using a building footprint file or a rectified orthophoto allows a georeference to a coordinate system (in our case NAD1983) and “spatially enables” our data.

Once georeferenced, we use ArcGIS to read the files in their native format and then we convert them to a shapefile format, which is much easier to handle in ArcGIS as well as our database. This conversion is executed by a simple tool used to process CAD files of this type in ArcGIS. Once the conversion is completed four shapefiles are produced for each CAD file and are separated by geometry type (point, line, multipatch, and polygon). The only one we are interested in is the polygon file that contains the rooms, stairways, and elevators, which are the main components in our system.

Once we have the polygon file we can then begin to remove all the extra data we do not need and begin to “clean” the files to contain only what we need. There is usually a tag in the attribute table of the shapefile that identifies the room, stairways, and elevator polygons. In our examples of Woodward Hall and Cameron Research Institute, these were labeled as “RM$”, and a simple SQL query allowed us to select them and insert them into their own separate file. This file is the one we use in the graph construction, visualization, and other analysis tasks of the building.

Once we have the “cleaned” files, they are uploaded into a central Postgresql with PostGIS database. This database serves as the central data server for the mobile and the desktop application, and allows updates to be propagated down the line to any device reading from it.