Generative AI for Enhanced Wildfire Detection: Bridging the Synthetic-Real Domain Gap

TL;DR

This paper introduces a novel approach using generative AI to synthesize annotated smoke datasets and applies domain adaptation techniques to improve wildfire detection accuracy, addressing data scarcity and domain gap challenges.

Contribution

It presents a comprehensive framework combining synthetic data generation and advanced domain adaptation methods for improved wildfire smoke detection.

Findings

Synthetic dataset significantly improves model training.

Domain adaptation reduces performance gap between synthetic and real data.

Generative techniques enhance realism and detection accuracy.

Abstract

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.