Real-Time Global Data Model for the Digital Earth

Nick Faust, William Ribarsky, T.Y. Jiang, and Tony Wasilewski

Electro-Optics, Environment, and Materials Laboratory
Georgia Institute of Technology

Email: nick.faust@gtri.gatech.edu

Link to full paper

To attack the problem of dealing with increasingly vast stores of global information for the digital earth, we describe in this paper a general approach to data organization and real-time exploration based on a novel global hierarchical data model. Our recent work has revealed that this framework can be quite generally applied to the earth and anything on it, above it, or even below it. This includes terrain elevations, phototextures and imagery, maps, buildings, moving or flying vehicles, weather, and other data. Further, the framework provides a geospatial visual data mining approach where one can navigate continously from global overviews to high resolution local views. Because it has an efficient data organization and paging capability closely coupled to a view-dependent data requesting mechanism (that is adjustable based on the requirements of the display platform), the framework is also quite flexible and has been applied to a range of single and networked systems ranging from a single-processor PC to immersive systems with multiple projection screens and coupled computers. In this paper we will discuss our geospatial model, how it fits into an overall framework, and how we have applied it to different types of data in different environments. We will also discuss our work towards distributed and Internet-based systems. Our approach starts with a hierarchical structure for optimal interactivity for data exploration. We use a novel "forest of quadtrees" with nested coordinate systems for handling global scale terrain data and permitting timely paging of collections of objects in support of real-time navigation. We have found that one can effectively employ this hierarchical structure for a wide range of geospatial data as long as long as methods adapted to the specific type of data (e.g., terrain versus buildings) are applied at the lower (detailed) levels. In this paper, we present an analysis of when this should occur and how large collections of objects, such as buildings and weather clouds, can efficiently fit into the geospatial hierarchy. The results demonstrate that our approach is both scalable and general because it is able to handle both large scale global terrain information and multiple collections of objects (e.g., cities and weather data) placed around the earth with full interactivity and without extensive memory load. Further the method shows efficient handling of culling. Finally, the method shows that levels of detail can be naturally incorporated to provide improved detail management.