  The explosive growth of spatial data and extensive utilization of spatial
databases emphasize the necessity for the automated discovery of spatial
knowledge. In modern times, spatial data mining has emerged as an area of
voluminous research. Forest fires are a chief environmental concern, causing
economical and ecological damage while endangering human lives across the
world. The fast or early detection of forest fires is a vital element for
controlling such phenomenon. The application of remote sensing is at present a
significant method for forest fires monitoring, particularly in vast and remote
areas. Different methods have been presented by researchers for forest fire
detection. The motivation behind this research is to obtain beneficial
information from images in the forest spatial data and use the same in the
determination of regions at the risk of fires by utilizing Image Processing and
Artificial Intelligence techniques. This paper presents an intelligent system
to detect the presence of forest fires in the forest spatial data using
Artificial Neural Networks. The digital images in the forest spatial data are
converted from RGB to XYZ color space and then segmented by employing
anisotropic diffusion to identify the fire regions. Subsequently, Radial Basis
Function Neural Network is employed in the design of the intelligent system,
which is trained with the color space values of the segmented fire regions.
Extensive experimental assessments on publicly available spatial data
illustrated the efficiency of the proposed system in effectively detecting
forest fires.
