199 
AN ENTERPRISE DATABASE-CENTRIC APPROACH 
FOR GEOSPATIAL IMAGE MANAGEMENT AND PROCESSING 
Qingyun (Jeffrey) Xie, Siva Ravada, Weisheng Xu, Zhihai Zhang 
Oracle USA, Inc., One Oracle Drive, Nashua, NH 03062, USA - 
(qingyun.xie, siva.ravada, weisheng.xu, zhihai.zhang)@oracle.com 
KEY WORDS: Spatial Databases, Raster Data, Data Management, Image Processing, Query Processing, Database, Software 
ABSTRACT: 
Geoimagery and raster gridded data are growing exponentially. As a result, we face many challenges. This paper discusses two of the 
major challenges. One is how to effectively and efficiently archive, manage, process and deliver all those geoimage data. Another 
challenge is how to make the geoimages and professionally extracted information available to broader audiences so that enterprises 
and mass consumers can benefit more from our work. This paper focuses on the database server technology, which is one of the key 
areas that is essential and foundational for solving the above two challenges and beyond. It proposes a new enterprise database 
centric approach for geospatial image and raster data management and processing. The key concept of this approach is to enhance 
and leverage enterprise IT technologies and provide a database-centric solution for image data management as well as key image 
processing operations. It consists of three major components: a new native database data type for raster data, a server-side image 
processing and raster operation engine and a standard-based user-friendly interface. It’s designed to work in a client-server 
environment as well as in any multi-tier architecture. The advantages and benefits of this approach are discussed. The Oracle Spatial 
GeoRaster was designed based on this approach. A series of tests and research using the Oracle GeoRaster technology were 
conducted and are partially presented in this paper. The results show that this approach is practical, easy-to-use and truly scalable and 
performant. This database-centric approach is a viable solution for geospatial image management and processing. 
1. INTRODUCTION 
Geoimagery and raster gridded data are growing exponentially. 
Numerous remote sensors of different types on various 
platforms are collecting real time data about the Earth and our 
environment for different purposes on a daily basis. As a result, 
we face many technical challenges, two of which are the major 
ones we need to address carefully. One is how to effectively and 
efficiently archive, manage, process and deliver all those 
geoimage data. This real-time processing, management and 
distribution task is becoming overwhelming and we have to 
solve it intelligently. Another challenge is how to make the 
geoimages and professionally extracted information available to 
broader audiences so that a variety of businesses and mass 
consumers can benefit more from our work. In other words, 
geospatial and geoimaging technologies need a good platform 
to go mainstream. New research and technologies are needed in 
order to better solve those problems and the existing database 
and application server and client technologies must work 
synergistically in order to achieve those goals. This paper 
focuses on the database server technology, which is one of the 
key areas that is essential and foundational for solving the 
above two challenges and beyond. 
It’s well known that enterprise database management systems 
provide great data security, reliability, availability, recovery and 
backup, transactional features, versioning and concurrency, to 
name just a few. Because of these benefits, spatial database 
technologies based on standard relational database management 
systems (RDBMS) have become very popular in recent years. 
Good examples include RasDaMan/RasGEO (Baumann, 2001) 
and ArcSDE (ESRI, 2005). They typically take a middleware 
approach by storing data inside a standard RDBMS system and 
processing the data in a middleware or client software package. 
Most RDBMS’s don’t have image data types defined. So this 
approach requires a relational database schema to be designed 
to store the imagery inside RDBMS. However, a fixed set of 
relational tables specified in such application schema doesn’t 
offer a good flexibility when it comes to integrate geoimage 
datasets with other enterprise datasets. The middleware acts as a 
processing engine, which queries the data from the RDBMS, 
retrieves the data out and then process the data in the 
middleware to serve the clients. Some extra data might have to 
be retrieved and delivered. Data transportation is expensive and 
could be insecure. So performance and data security are 
concerns with this approach. The other downside is either the 
lack of standard database SQL interface or the decoupling of its 
interface from the RDBMS system, which significantly limits 
the usability and enterprise integration efforts. 
In this paper, we describe an enterprise database-centric 
approach for geospatial image and raster data management and 
processing. The key concept of this approach is to enhance and 
leverage enterprise IT technologies and provide a database 
centric solution for image data management as well as key 
image processing operations. It not only offers the 
aforementioned standard database benefits but also goes one 
step further to provide more advantages to tackle the specific 
requirements derived from the two major challenges facing the 
geoimaging and geospatial industry. 
2. THE ENTERPRISE DATABASE-CENTRIC 
APPROACH AND THE BENEFITS 
While it’s been proven to have many advantages and have 
become the industry trend to manage geoimagery data in 
commercial RDBMS systems instead of directly using file 
systems, we think the traditional RDBMS system itself should 
be enhanced to specifically handle geoimagery within the 
databases as well.