Forest Wildfire Detection from Satellite Image Using Deep Learning

This study presents an improved methodology of wildfire detection in the forest using PyTorch, a wise machine learning framework, and the Flask application for real-time monitoring. Focusing on accuracy and speed in detection, the methodology mainly refers to data acquisition, and preprocessing of satellite image datasets in its essence, meanwhile featuring the training of robust models through data augmentation. The experimental results using the PyTorch model, which depends on DenseNet201, have proven its ability to determine patterns and reflect highly dynamic contrasts between vertical and horizontal pans; this is especially true when the principal element of the considered satellite image is diffuse and indeterminate. The framework has been integrated into a user-friendly Flask web app, and it gives outputs of immediate wildfire detection upon image upload. The approach is also shown to deliver better metrics thus a more efficient solution as proven by the experimental results. The study informs environmental technology about introducing an easy-to-apply, applicable, and adaptive tool that may have potential associations with environmental agencies for environmentally sustainable approaches to wildfire management.