Abstract
Today, spatial data mining (SDM) techniques are used collectively with GIS and satellite imagery to deduce associations between spatial attributes, cluster and classify information related to spatial attributes in various fields such as fire accident analysis, forest extent change analysis, agricultural land classification, agriculture and forestry, soil quality monitoring, urban area classification, and meteorology, etc. GIS has become indispensable and contains heterogeneous data from multidisciplinary sources in different formats. Today, thanks to the increasing power of remote sensors and the improvement of GIS technologies themselves, the amount of terrestrial data generated is very massive. The rapid growth of geographic databases and satellite imagery is generating a huge volume of data related to natural resources such as vegetation, water, temperature, forest cover, urbanism, etc. The objective of this paper is to study the different spatial data mining (SDM) techniques for the analysis of data related to spatial relationships. This article presents a description of SDM tasks and gives the idea to understand GIS data models. It also presents the different GIS data sources, and data formats and describes the challenges related to the GIS dataset.
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