Analysis of Deep Learning Architectures and Efficacy of Detecting Forest Fires
It addresses the problem of guiding new machine learning practitioners in forest fire detection, but is incremental as it focuses on reviewing and introducing existing resources.
This research reviews computer vision methods for detecting forest fires, aiming to introduce domain-specific datasets and methods to make them more accessible to practitioners lacking expertise, but does not report specific results or numbers.
The aim of this research is to review the state of computer vision as applied to combatting forest fires. My motivation to research this topic comes from the urgency with which new participants and stakeholders require guidance in this field. One of these new stakeholder groups are practitioners of machine learning that lack domain expertise. Introducing these new entrants to domain specific datasets and methods is critical to supporting this aim as general computer vision datasets are insufficient to support specialized research initiatives. The overarching aim of the research is to introduce datasets and methods to make them more accessible to the community.