Data related to various natural, anthropogenic and socio-economic phenomena are now available in numerous formats, most significant of which is spatial data that facilitate visualization at spatio-temporal intervals. Availability of such data from a wide range of sources in a variety of formats poses challenges to Geographic Information Science (GISci) community. The utility and application of such data could be substantially enhanced through developments in technologies related to:

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Retrieval: Retrieval of noise-free information in the forms of themes (layers) from data requires robust image processing, spatial information theory techniques.
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Analysis: Once theme-specific layered information is retrieved, techniques are required to analyse themes.
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Reasoning: Theme specific layered information need to be integrated via spatial relationships and reasoning. Certain map algebraic concepts are of use.

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Modelling: Spatio-temporal behaviour of a phenomenon needs to be visualized

To retrieve noise-free phenomena to represent them in layered forms, which are basic inputs in GIS, to develop application specific information systems, challenges are still unresolved. Sequel to these challenges, analyses of layered information to overcome constraints posed by restrictions due to spatio-temporal resolution. Establishing spatial relationships across mapped layered information via spatial reasoning is still at the research level. Once, the robust strategies to retrieve, analyse, reason the information at multiscale and multitemporal modes are available, modelling the spatio-temporal behaviour of a phenomenon would be rather straightforward. It is realized that the better thematic retrieval procedures, and further analysis and reasoning would pave a way to better deal with the noise-free layered spatial maps in the context of modelling via GISci.
Much success has been achieved in the proper usage of data by addressing the above four aspects by individual groups. It is now at understandable level and there are overlaps between the concepts that emerged from different fields to deal with the above four aspects. In light of these overlaps, there exist demands to choose appropriate mathematical techniques that can offer robust solutions. As it stands, there are various techniques (e.g. mathematical morphology, fuzzy set theory, rough set theory, granular computing, map algebra) to address the challenges.

The motivation stems from the following observation. For groups, which are familiar with both spatial information theory and theories involved in digital image processing and analysis, most of these ideas are quite familiar. But, surprisingly there has been little interaction between the groups respectively familiar with image processing and spatial information theory. This seminar is intended to serve as a forum for bringing together specialists in those two groups and facilitate interaction.