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<p>Automated image-based wildfire detection suffers from a lack of open-access data, especially data with annotations. Our dataset targets the gap by providing human and computer vision foundation-model co-annotated images from an uncrewed aerial vehicle (UAV) perspective from Finnish boreal forest environments. The images and videos were collected at multiple prescribed burning events, and the data were used to successfully train wildfire detection models in our previous studies, proving their value for the task. The Boreal Forest Fire dataset contains three sections: images with bounding box annotations, video clips with labels and images with segmentation masks. Alongside the data, we have released code, ensuring that the data is simple to use.</p>

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Boreal Forest Fire: UAV-collected Wildfire Detection and Smoke Segmentation Dataset

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月13日

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  • Julius Pesonen,
  • Anna-Maria Raita-Hakola,
  • Jukka Joutsalainen,
  • Teemu Hakala,
  • Waleed Akhtar,
  • Niko Koivumäki,
  • Lauri Markelin,
  • Juha Suomalainen,
  • Raquel Alves de Oliveira,
  • Ilkka Pölönen,
  • Eija Honkavaara

摘要

Automated image-based wildfire detection suffers from a lack of open-access data, especially data with annotations. Our dataset targets the gap by providing human and computer vision foundation-model co-annotated images from an uncrewed aerial vehicle (UAV) perspective from Finnish boreal forest environments. The images and videos were collected at multiple prescribed burning events, and the data were used to successfully train wildfire detection models in our previous studies, proving their value for the task. The Boreal Forest Fire dataset contains three sections: images with bounding box annotations, video clips with labels and images with segmentation masks. Alongside the data, we have released code, ensuring that the data is simple to use.