Application of deep learning and satellite data for monitoring and forecasting forest fires in Russia: performance and perspective analysis

Abstract

This study analyzes the effectiveness and prospects of using deep learning technologies and satellite data for monitoring and forecasting forest fires in Russia in the period from 2017 to 2023. The relevance of the topic is due to the growing threat of forest fires to the country's ecosystems and economy, as well as the rapid development of artificial intelligence methods in the field of environmental monitoring. The aim of the work is to comprehensively assess the effectiveness of existing AI systems for detecting forest fires and identify key factors affecting their effectiveness in Russia. The research methodology includes the collection and analysis of big data on forest fires from satellite images, reports from the Ministry of Emergency Situations and scientific publications, as well as the use of deep learning and statistical modeling methods to identify patterns and make forecasts. The results show a significant increase in the accuracy and speed of forest fire detection using convolutional neural networks compared to traditional monitoring methods. At the same time, the effectiveness of AI systems varies significantly across the regions of Russia, depending on the landscape features and the level of technical equipment of forestry enterprises. The study also identified key problems in the implementation of deep learning technologies related to the shortage of labeled data and computing power. The results obtained are of high practical importance for optimizing the forest fire monitoring system and can be used in developing strategies for the development of Russian forestry. In the long term, the study lays the foundation for the creation of an integrated AI platform for forecasting and early detection of wildfires in the country.