Agent-based approaches have not yet been widely applied to highly complex, data intensive,large-scale information processing systems such as are found in the domain of imagery & geospatial computing. Such systems combine diverse and distributed types of imagery and geospatial data, and require collaboration from multiple experts and processing components. This paper gives a description of the d...

Disaster prevention and management is a complicated task that often involves a tremendous volume of heterogeneous data from various resources. With its dynamic and distributed nature, service technology appears to be a good candidate for removing the communication barriers between various organizations and for facilitating the integration and sharing of distributed data. For it to succeed, howe...

With the advent of the World Wide Web (WWW), the way people publish, retrieve and synthesise information has totally changed. The Web is a hypertext system consisting of a huge amount of information which has very different forms. The concept of geospatial Web content emphasises the geospatial context of online information. Due to the continued explosive increase in the volume and complexity of...

Data warehouses are being considered as substantial elements for decision support systems. They are usually structured according to the multidimensional paradigm, i.e. datacubes. Geospatial datacubes contain geospatial components that allow geospatial visualization and aggregation. However, the simultaneous use of multiple geospatial datacubes, which may be heterogeneous in design or content, d...

Geospatial feature discovery from remote sensing imageries is widely used in national defense and security communities. Existing methods in the geospatial image mining and feature extraction focus on the manual or automated processing of images to detect individual elementary features, such as building and highway. Such elementary features don’t tell much semantic information about the features...

The paper, focusing on the problem that the overall correlation level of the research result is low in the current geospatial data search system, establishes users’ search intention models, researches the intelligent and professional geospatial information search method, and makes use of spatial cognition theory and knowledge engineering methods to make up for the shortcomings of the lack of se...

To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the ...

With the development of its economy in China, the demand for geospatial applications in various fields has been greatly increased in recent years. And the geospatial Industry has been emerged as new and active factor in information service sector. During the past ten years, thousands of companies and institutes have involved in the business for technology development and provide services. It is...

Disasters are events that cross geographical, organisational and other human-made or natural boundaries. Therefore, disaster management requires that various geospatial data producers and users collaborate together in an ad hoc manner, despites their different background and contexts. While semantic interoperability is meant to support meaningful communication between geospatial databases, sema...

Geospatial clustering is a very important research topic in the field of „Geospatial Knowledge Discovery.‟ Its main purpose is to group similar objects, from large geospatial datasets, into clusters, based on the objects‟ spatial and non-spatial attributes. This capability is very useful for better understanding the patterns and distributions of geographical phenomena. With the emerging availab...