Home Articles Future Trends In Geospatial Data Management Process

Future Trends In Geospatial Data Management Process

Squadron Leader Mudit Mathur
Squadron Leader Mudit Mathur
Indian Air Force
[email protected]

Introduction
A fantastic challenge for science is to understand the human entailments of global environmental & situational change and to help society cope with those changes. Virtually all the scientific questions associated with this challenge depend on geospatial information (geo-information) and on the power of workforce of scientific discipline, working individuals and radicals, to interact with that information in flexible and increasingly complex ways. Another grand challenge is how to respond to calamities, force majeure—terrorist actus reus, other human-stimulated crossroadses, and natural disasters. Much of the information that substantiates in emergency preparedness, qui vive, response, recovery, convalescence and mitigation is geospatial in nature. In terrorist situations, for example, origins and destinations of phone calls and e-mail messages, travel patterns of individuals, dispersal patterns of airborne chemicals, assessment of places at risk, and the allocation of resources all involve geospatial information. Much of the work addressing environment- and emergency-akin vexations will depend on how productively humans are able to incorporate, distill, and correlate a wide range of seemingly unrelated untangled information. In addition to critical advances in location-cognisant figuring, databases, and data mining methods, advances in the human-computer interface will couple new computational capabilities with human cognition & noesis capabilities and technology inter & intra disciplinary dependence thrust.

This paper outlines an interdisciplinary roadmap at the intersection of computer science and geospatial information science. With the rapid development of space technology and convenient use of space, considerable accelerated growth in the field of space borne Remote Sensing (RS) has taken in past decade. In majority of nations across the globe multiple Terabytes of space borne remote sensing valuable data in form of Imageries and metadata are collected from diverse and complex platforms. The question is how to process, productively manage, effectively archive, make dependable use of the RS information and share such a vast amount of RS images and information entropy efficiently expeditiously, apace, and provide benefits to public at large keeping in a complex security questions answered. A comprehensive storage, query and data security mechanism is necessary to organize productively manage & effectively manage the data in a way that they are easily accessible.

Geo-database system
A RS image management system is a system that can provide the functionalities and “colligate” the image providers with the image users. Presently Geo-database technology is outperforming the capabilities of a simple data management system, and becoming progressively a system, tightly integrated with complementary engineering sciences such as telecommunications, networks, multimedia, artificial intelligence, neural networks, natural language knowledge engineers and so on. Furthermore, as a recent development in database systems, more and more systems such as Oracle and Microsoft SQL Server provide for new media object types, e.g., the Binary Large Object (BLOB) and Binary File (BFILE). Such a new capability makes it possible to develop a “database-based”, or “spatially-enabled”, image information management system.

In file-based systems, images are managed in a flat file, and organized either by a directory structure or by a file server on a computer. Generally, a file-based image system is acceptable for small and moderate data volume applications. Nevertheless, when it comes to data volumes of the order of terabytes, such systems are ineffective. In this system, a high-resolution image covering a large area may have to be stored in several files because of the limitation of operation system. This makes data management and data retrieval more complicated, and becomes a barrier for the implementation of a high data-volume RS image system.