Wildfire Spread Modeling with Aerial Image Processing
Wildfire Spread Modeling with Aerial Image Processing
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DOI：
10.1109/wowmom49955.2020.00063
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Qiyuan Huang;Abolfazl Razi;F. Afghah;P. Fulé
中科院分区：
文献类型：
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作者：
Qiyuan Huang;Abolfazl Razi;F. Afghah;P. Fulé
目前，野火蔓延建模已经引起了研究界的广泛关注，因为每年许多国家都遭受野火的严重社会经济影响。火灾蔓延建模是有效火灾管理的关键要求，可以在正确的时间和位置部署消防设备和部队，并计划及时疏散居民区。本文提出了一种新的数据驱动的火灾扩展模型，使用基于参考的图像分割植被密度估计，并将其纳入火灾热传导建模。与传统的火灾现场参数采集方法相比，该方法依赖于无人机拍摄的俯视图像，具有灵活、安全、低成本、方便等显著优势。我们的低复杂性和概率模型将地形坡度、植被密度和风因子与可调模型参数结合起来，这些参数可以很容易地从实验中学习。该模型具有一定的灵活性，适用于混合植被和不同地理气候条件的森林。我们通过将结果与2013年加州边缘火灾的传播数据进行比较来评估火灾传播模型。
Currently, wildfire spread modeling has drawn a lot of attention from the research community since many countries are suffering from severe socioeconomic impacts of wildfires, every year. Fire spread modeling is a key requirement for effective fire management to deploy fire control equipment and forces at the right time and locations, and plan timely evacuations of residential areas. This paper proposes a new data-driven model for fire expansion which uses reference-based image segmentation for vegetation density estimation and incorporates it into the fire heat conduction modeling. Compared with the conventional parameter collection methods at fire scenes, our method relies on topview images taken by unmanned aerial vehicles, which provides significant advantages of flexibility, safety, low cost, and convenience. Our low-complexity and probabilistic model incorporates the terrain slope, vegetation density, and wind factors with adjustable model parameters which can be easily learned from experiments. The proposed model is flexible and applicable to forests with mixed vegetation and different geographical and climate conditions. We evaluate the fire propagation model by comparing the results with the propagation data available for California Rim fire in 2013.