Vulnerability Analysis and Quality Improvement of Early Wildfire Detection Datasets for Machine-Learning Applications

Wildfires are recognized as highly devastating natural disasters, capable of causing irreversible harm to the environment, structures, and human lives and the aftermath of this dreadful occurrence often involves exorbitant expenses for repairs. Detecting wildfires poses a significant challenge, but identifying scenarios for early detection could empower cities and countries to proactive prepare strategies for wildfire management. The primary objective of this research paper is to provide analysis and improvement of the major vulnerabilities of existing public datasets for early wildfire detection using machine learning. We discuss the current weaknesses in these datasets and illustrate their impacts on machine learning solutions. We then describe the required dataset quality aspects and discuss their advantages for early wildfire detection using machine learning. A sample quality dataset and a sample deep learning model using TensorFlow are made publicly available through GitHub.