Satellite Image-Based Surveillance and Early Wildfire Smoke Detection Using a Multiattention Interlaced Network

The increasing frequency and intensity of wildfires in recent years have not only devastated forest ecosystems, but have also caused a significant economic burden. According to a World Economic Forum report, annual expenditures to combat wildfire hazards is estimated to be more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ {\$} 50$</tex-math></inline-formula> billion. This calls for advanced solutions, such as remote sensing surveillance and the use of artificial intelligence for wildfire management. In recent years, several vision-based artificial intelligence techniques have been proposed for fire–smoke image classification that utilise convolutional neural networks. However, challenges persist, particularly in identifying fire–smoke under complex atmospheric conditions. In this article, we introduce a novel multiattention network that interlaces the vision transformer and convolutional neural network to detect fire–smoke in diverse conditions, including clouds, fog, hurricanes, storms, snow, and normal weather. The proposed model not only outperforms eight state-of-the-art fire–smoke image classification methods, but also reduces false alarms by 30% on IIITDMJ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\_$</tex-math></inline-formula> Smoke dataset and by 6% on UTSC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\_$</tex-math></inline-formula> SmokeRS dataset. The model also efficiently identifies even tiny occurrence of smoke covering as little as 2% area of an image. The model has also been tested on industrial chimney smoke images and outdoor video fire–smoke scenes. Furthermore, the lightweight architecture of the model with only 0.7 million parameters and 0.2 billion floating point operations per second makes it suitable for deployment on Internet of Things-based forest and industrial surveillance systems.