Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand

--Forest fires pose a significant threat to the environment, human life, and property. Early detection and response are crucial to mitigating the impact of these disasters. However, traditional forest fire detection methods are often hindered by our reliability on manual observation and satellite imagery with low spatial resolution. This paper emphasizes the role of transfer learning in enhancing forest fire detection in India, particularly in overcoming data collection challenges and improving model accuracy across various regions. We compare traditional learning methods with transfer learning, focusing on the unique challenges posed by regional differences in terrain, climate, and vegetation. Transfer learning can be categorized into several types based on the similarity between the source and target tasks, as well as the type of knowledge transferred. One key method is utilizing pre-trained models for efficient transfer learning, which significantly reduces the need for extensive labeled data. We outline the transfer learning process, demonstrating how researchers can adapt pre-trained models like MobileNetV2 for specific tasks such as forest fire detection. India is home to a vast and diverse range of forests, covering over 70 million hectares of land [1]. These forests are crucial not only for the country's ecosystem and biodiversity but also provide livelihoods for millions of people, particularly in rural areas. However, India's forests are facing a growing threat from forest fires, which can have devastating consequences for the environment, human life, and property [2]. Forest fires are a major concern in India, particularly during the summer months when temperatures are high and humidity is low. According to the Indian government, forest fires affect over 50, 000 hectares of land annually, causing significant economic losses and damage to the environment [3]. The country's forests are also home to a wide range of wildlife, including many endangered species which are threatened by fires. Figure 1 illustrates some images of the Uttarakhand, India, forest fire. Early detection and response are critical to mitigating the impact of forest fires. Traditional methods of forest fire detection, such as manual observation and satellite imagery with low spatial resolution, are often limited in their ability to detect fires quickly and accurately [4]. Manual observation is time-consuming and labour-intensive and may not be feasible in remote or inaccessible areas [5]. Satellite imagery with low spatial resolution may not be able to detect small fires or fires in areas with dense vegetation. In recent years, advances in deep learning and computer vision have enabled the development of more effective methods for forest fire detection. Convolutional neural networks (CNNs), in particular, have shown great promise in image classification tasks [6]-[10], including fire detection [4].