Detecting Wildfires Using Satellite Imagery

The research “Wildfire Detection from Satellite Images” addresses the formidable challenge of automating the classification of diverse visible range satellite images through an innovative integration of machine learning and computer vision using custom dataset. In contrast to conventional methods relying on manual observations, this pioneering venture leverages a meticulously curated dataset representing a broad spectrum of satellite images in various environmental contexts. The binary classification model, at the research's core, combines the strengths of diverse machine learning algorithms, employing ensemble learning techniques to enhance accuracy and versatility across different ecological conditions. Climate action is a key Sustainable Development Goal (SDG-13) of the United Nations (UN), and wildfires are one of the calamitous events that is both caused by and contribute to climate change. Large scale wildfires have increased significantly in frequency in Australia and other wildfire prone nations in recent years. These fires pose a threat to urban and global forests, inflict a significant amount of harm to property and the environment, and frequently result in fatalities. There is a continuous discussion about how to manage wildfires and lessen their social, economic, and environmental effects. With different rising monitoring systems and various applications of those system emerge to observe the ecology and environment of the globe. This research showcases the intricate relation of technology with nature. By deploying machine learning/ deep learning model to observe different events of the globe, we can easily develop system to detect any drastic climate event such as Wildfires and take preventive measures at early stages reducing causality and losses.