构建首个全球多国烟雾分割数据集，提升火灾检测模型泛化能力。
AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation dataset
- 整合澳洲实地采集与国际公开数据，构建跨区域烟雾图像集。
- 数据量扩大一个数量级，覆盖多国真实烟雾场景。
- 适合从事火灾监测、遥感分析的科研与工程人员使用。
野火因对环境、经济和人类健康的严重危害而成为日益严峻的全球性问题，2019-2020年澳大利亚山火及2025年加州山火凸显了其严重性。基于AI的摄像头烟雾检测已成为快速发现野火的有前景方法。然而，现有用于训练检测与分割模型的野火烟雾分割数据集规模有限、地理分布单一，且常依赖合成图像，制约了模型的有效训练与泛化能力。为克服这些局限，我们提出AusSmoke，一个从澳大利亚收集的烟雾分割数据集，以填补该地区数据空白。进一步，我们构建了一个名为MultiNatSmoke的跨国、大规模、全标注基准数据集，整合了公开的国际数据与新采集的澳大利亚影像，使数据规模较以往集合扩大一个数量级。最后，我们对烟雾分割模型进行基准测试，验证了其在多样地理背景下的性能提升与泛化增强。项目开源于Github。
原文摘要 · Abstract (English)
Wildfires are an escalating global concern due to the devastating impacts on the environment, economy, and human health, with notable incidents such as the 2019-2020 Australian bushfires and the 2025 California wildfires underscoring the severity of these events. AI-enabled camera-based smoke detection has emerged as a promising approach for the rapid detection of wildfires. However, existing wildfire smoke segmentation datasets that are used for training detection and segmentation models are limited in scale, geographically constrained, and often rely on synthetic imagery, which hinders effective training and generalization. To overcome these limitations, we present AusSmoke, a new smoke segmentation dataset collected from Australia to address the data scarcity in this region. Furthermore, we introduce a MultiNational geographically diverse and substantially larger fully-labelled benchmark, called MultiNatSmoke, that consolidates publicly available international datasets with the newly collected Australian imagery, expanding the scale by an order of magnitude over previous collections. Finally, we benchmark smoke segmentation models, demonstrating improved performance and enhanced generalization across diverse geographical contexts. The project is available at \href{https://github.com/henryzhao0615/MultiNatSmoke}{Github}.
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