Over the past decade, forest fires have caused devastation in many areas of India, severely harming forest ecosystems, reducing biodiversity, and affecting the lives of populations that depend on the forests for their subsistence. Convolutional Neural Networks (CNNs), or ConvNets, represent a specialized deep learning architecture that extracts and learns patterns directly from data. CNNs are excellent at recognizing patterns in images, allowing them to identify objects, group similar items, and classify different categories with high precision. They can also be highly effective at classifying audio, time-series, and signal data. This work suggests creating a model that can be used to classify whether or not there is forest fire based on the images. In order to get better outcomes, the deep neural network component of the final model was developed from the VGG16 basic architecture. 5062 photos from open source sources, including both fire and no-fire conditions, were used to train the model. This paper presents a model developed using Keras with TensorFlow and Theano as the backend and the efficiency of the model was compared. The TensorFlow based model provided an accuracy of 97.6% and the Theano based model provided an accuracy of 97.54%. Even with limited resolution, the model with Keras and TensorFlow backend was able to categorise the majority of the random pictures given to it as Fire(1) and No Fire(0) class with better evaluation scores and less time.

CNN, Deep Learning, VGG16, Forest Fires, Keras, TensorFlow, Theano, Image classification

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