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Related Experiment Video

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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation.

Jun Mao¹, Change Zheng¹, Jiyan Yin²

¹School of Technology, Beijing Forestry University, Beijing 100083, China.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

This study introduces a novel method using synthetic wildfire smoke images to train deep learning models. The approach achieves 97.39% accuracy in classifying real wildfire smoke, addressing data scarcity and diversity issues.

Keywords:

adversarial training deep learning domain adaptation synthetic images wildfire smoke classification

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Area of Science:

Computer Science
Environmental Science
Artificial Intelligence

Background:

Deep learning models for wildfire smoke classification require extensive, diverse datasets.
Acquiring real-world wildfire smoke imagery is challenging due to event unpredictability and limited sample diversity.

Purpose of the Study:

To develop a method for training effective wildfire smoke classification models using synthetic data.
To overcome limitations of insufficient and non-diverse real-world wildfire smoke image datasets.

Main Methods:

A synthetic dataset was created using 3D modeling for diverse smoke morphologies and wildland backgrounds.
Pixel-level domain adaptation (CycleGAN) and feature-level domain adaptation (ADDA with DeepCORAL) were applied to bridge the synthetic-real data gap.

Main Results:

The proposed method achieved a high accuracy of 97.39% on a real wildfire smoke test set.
Demonstrated the efficacy of domain adaptation techniques in leveraging synthetic data for wildfire smoke classification.

Conclusions:

Synthetic data, enhanced by domain adaptation, can effectively train deep learning models for wildfire smoke detection.
This approach offers a viable solution for wildfire smoke classification tasks with limited real-world data.