Enhancing Wildfire Detection through Semi-Supervised Domain Adaptation and Diverse Labeling

This paper proposes a novel semi-supervised domain adaptation framework, LADA, that leverages a large amount of unlabeled target data and a minimal set of labeled target data to significantly improve wildfire detection performance. The authors also introduce a new benchmark dataset with 30 times more diverse labeled scenes compared to the current largest wildfire dataset.