Generative AI for Enhanced Wildfire Detection: Bridging the Synthetic-Real Domain Gap
This work addresses the critical environmental challenge of early wildfire detection for disaster mitigation, but it appears incremental as it builds on existing generative and domain adaptation methods.
The paper tackled the problem of limited annotated datasets for wildfire smoke detection by using generative AI to synthesize a comprehensive dataset and applying unsupervised domain adaptation and advanced generative techniques to bridge the synthetic-real domain gap, aiming to improve detection accuracy and scalability.
The early detection of wildfires is a critical environmental challenge, with timely identification of smoke plumes being key to mitigating large-scale damage. While deep neural networks have proven highly effective for localization tasks, the scarcity of large, annotated datasets for smoke detection limits their potential. In response, we leverage generative AI techniques to address this data limitation by synthesizing a comprehensive, annotated smoke dataset. We then explore unsupervised domain adaptation methods for smoke plume segmentation, analyzing their effectiveness in closing the gap between synthetic and real-world data. To further refine performance, we integrate advanced generative approaches such as style transfer, Generative Adversarial Networks (GANs), and image matting. These methods aim to enhance the realism of synthetic data and bridge the domain disparity, paving the way for more accurate and scalable wildfire detection models.