Semi-Supervised Domain Adaptation for Wildfire Detection

• URL: http://arxiv.org/abs/2404.01842v1
• Date: Tue, 2 Apr 2024 11:03:13 GMT
• Title: Semi-Supervised Domain Adaptation for Wildfire Detection
• Authors: JooYoung Jang, Youngseo Cha, Jisu Kim, SooHyung Lee, Geonu Lee, Minkook Cho, Young Hwang, Nojun Kwak,
• Abstract summary: We propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection. Our dataset encompasses 30 times more diverse labeled scenes for the current largest benchmark wildfire dataset, HPWREN. Our framework significantly outperforms our source-only baseline by a notable margin of 3.8% in mean Average Precision.
• Score: 20.86166825570607
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes for the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection. Inspired by CoordConv, we propose a robust baseline, Location-Aware Object Detection for Semi-Supervised Domain Adaptation (LADA), utilizing a teacher-student based framework capable of extracting translational variance features characteristic of wildfires. With only using 1% target domain labeled data, our framework significantly outperforms our source-only baseline by a notable margin of 3.8% in mean Average Precision on the HPWREN wildfire dataset. Our dataset is available at https://github.com/BloomBerry/LADA.

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