# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
###### Abstract  
Analysis of reports published by the leading national centers for monitoring wildfires and other emergencies revealed that the devastation caused by wildfires has increased by 2.96-fold when compared to a decade earlier. The reports show that the total number of wildfires is declining; however, their impact on the wildlife appears to be more devastating. In recent years, deep neural network models have demonstrated state-of-the-art accuracy on many computer vision tasks. In this paper, we describe the design and implementation of a lightweight wildfire image classification model (LW-FIRE) based on convolutional neural networks. We explore different ways of using the existing dataset to efficiently train a deep convolutional neural network. We also propose a new method for dataset transformation to increase the number of samples in the dataset and improve the accuracy and generalization of the deep learning model. Experimental results show that the proposed model outperforms the state-of-the-art methods, and is suitable for real-time classification of wildfire images. artificial neural networks; computer vision; machine learning; deep learning; deep neural networks; image classification; convolutional neural networks +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON] [PERSON]; LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network. _Appl. Sci._**2022**, 12, 2646. [[https://doi.org/10.3390/app12052646](https://doi.org/10.3390/app12052646)]([https://doi.org/10.3390/app12052646](https://doi.org/10.3390/app12052646))  
Academic Editors [PERSON], [PERSON] and [PERSON]  
Received: 10 February 2022  
Accepted: 26 February 2022  
Published: 4 March 2022  
**Published:**  
Furthermore, there is also a growing awareness of the economic and social benefits associated with forests [4]. The methods for automatic wildfire detection, diagnosis, and prevention have been gaining more and more attention over the years. At the same time, remote sensing technologies and geographic information systems, as well as processes for data acquisition, are more accessible and versatile [5]. The main research challenges are associated with adequately analyzing and extracting knowledge from various sources of data. Continuous improvements in the field of artificial intelligence (AI), especially machine learning (ML), provide ample opportunities to design algorithms and methods to solve domain-specific problems, including the aforementioned problem of extracting knowledge from image data. In recent years, computer vision-based wildfire prediction and detection systems have become very popular due to the rapid emergence and growth of technologies capable of analyzing large amounts of digital images with different artificial intelligence methods. Machine learning and deep learning (DL) methods are being increasingly applied to wildfire prediction and detection [6; 7; 8; 9]. Unmanned aerial vehicles (UAVs) equipped with modern  
Figure 1: The number of wildfires (**left**) and acres burned (**right**) in the USA from 2010 to 2021. The information is collected from the interagency Fire and Aviation Management Web Applications (FAMWEB) system. The data for 2021 are from the period January 1 to 29 October 2021 (marked in red). Data for the last two months of the year 2021 are not available yet. Figure 2: Average number of acres burned by wildfires from 2010 to 2020. The number of fires decreased, but the effect of those fires is more devastating—more acres are burned by fewer fires. computer vision-based systems have great potential for detecting, monitoring, and fighting wildfires. A system for the automatic detection of wildfires is recommended for any wildlife preservation and protection organization, forestry management group, wildfire research organization, and other government or non-government agency. In this paper, we propose a new method for the automated classification of wildfire images based on a modern deep learning approach for image classification.