Fire Hazard Research of Forest Areas based on the use of Convolutional and Capsule Neural Networks

Abstract

The scientific and practical problem of detecting fire hazardous forest areas by using deep learning artificial neural networks applied to Camp Fire (California, USA) is considered in the paper. The theory of deep learning neural networks, the theory of recognition multispectral images and mathematical statistics methods are used. A novel solution of the multispectral images recognition method by using capsule and convolutional neural networks applied to Camp Fire area is presented. A comparative analysis of convolutional and capsule neural networks is conducted.