An Intelligent System For Effective Forest Fire Detection Using Spatial Data

TL;DR

This paper introduces an AI-based system utilizing image processing and neural networks to detect forest fires from spatial data, enhancing early detection capabilities in remote and vast areas.

Contribution

The novel approach combines color space transformation, anisotropic diffusion segmentation, and RBF neural networks for accurate forest fire detection from spatial images.

Findings

Effective fire region identification through color space analysis.

High accuracy in fire detection demonstrated in experiments.

System suitable for real-time monitoring in remote areas.

Abstract

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.