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

Satyam Gaba

TL;DR

The paper tackles wildfire smoke segmentation under data scarcity by generating synthetic labeled smoke data and applying unsupervised domain adaptation to real images from ALERTCalifornia. It systematically evaluates AdaptSegNet and AdvEnt, then investigates bridging the synthetic-real gap with style transfer, Pix2Pix, CycleGAN, and image matting. Findings show that UDA provides limited improvements and GAN-based translations introduce artifacts, while deep image matting—despite requiring manual trimaps—offers the most promising route toward realistic composites and improved domain alignment. The work highlights data-centric strategies and points to automating trimap generation and exploring semi-supervised approaches as promising directions for scalable wildfire smoke detection.

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.

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

TL;DR

The paper tackles wildfire smoke segmentation under data scarcity by generating synthetic labeled smoke data and applying unsupervised domain adaptation to real images from ALERTCalifornia. It systematically evaluates AdaptSegNet and AdvEnt, then investigates bridging the synthetic-real gap with style transfer, Pix2Pix, CycleGAN, and image matting. Findings show that UDA provides limited improvements and GAN-based translations introduce artifacts, while deep image matting—despite requiring manual trimaps—offers the most promising route toward realistic composites and improved domain alignment. The work highlights data-centric strategies and points to automating trimap generation and exploring semi-supervised approaches as promising directions for scalable wildfire smoke detection.

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.

Paper Structure

This paper contains 28 sections, 16 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional methods
  4. Smoke Segmentation networks
  5. Domain Adaptation for Semantic Segmentation
  6. Smoke Segmentation using Domain Adaptation
  7. Method
  8. Synthetic Data Generation
  9. Transfer learning
  10. Unsupervised Domain Adaptation
  11. AdaptSegNet
  12. AdvEnt
  13. Bridging the Domain gap
  14. Style Transfer
  15. Pix2Pix GAN
  16. ...and 13 more sections

Figures (16)

  • Figure 1: Examples of real wildfire images captured by cameras deployed for the ALERTCalifornia Project. The variety in these images introduces significant challenges for the segmentation task. Images are taken at different times, from various cameras positioned in different locations. Challenges include occlusions (b), small or distant smoke plumes (c), low lighting conditions (d), and haze caused by smoke (e).
  • Figure 2: Example of synthetically generated data from smoke and non-smoke images
  • Figure 3: Manually annotated smoke data using VGG Annotation tool vgg_ann.
  • Figure 4: AdaptSegNet model architecture, adapted from tsai2020learning
  • Figure 5: AdvEnt model architecture, taken from vu2019advent
  • ...and 11 more figures