Semi-Supervised Domain Adaptation for Wildfire Detection (Papers Track)

Joo Young Jang (Alchera); Youngseo Cha (Alchera); Jisu Kim (Alchera); SooHyung Lee (Alchera); Geonu Lee (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera); Nojun Kwak (Seoul National University)

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Unsupervised & Semi-Supervised Learning Disaster Management and Relief Computer Vision & Remote Sensing


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 than 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 self-supervised Domain Adaptation framework capable of extracting translational variance features characteristic of wildfires. Our framework significantly outperforms the existing baseline by a notable margin of 3.8\%p in mean Average Precision on the HPWREN wildfire dataset.

Recorded Talk (direct link)