AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation dataset
Weihao Li, Hongjin Zhao, Gao Zhu, Ge-Peng Ji, Nicholas Wilson, Marta Yebra, Nick Barnes

TL;DR
This paper introduces AusSmoke and MultiNatSmoke, two large, diverse smoke segmentation datasets to improve wildfire detection models' training and generalization across different regions.
Contribution
The paper presents a new Australian smoke dataset and a comprehensive international benchmark, significantly expanding data diversity and scale for wildfire smoke segmentation.
Findings
Benchmarking shows improved segmentation performance across diverse datasets.
MultiNatSmoke enhances model generalization to different geographical regions.
The datasets facilitate better training for AI-based wildfire detection systems.
Abstract
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…
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