期刊
APPLIED SCIENCES-BASEL
卷 11, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/app112211060
关键词
wildfire severity prediction; deep neural networks; multi-channel attention-based analysis
类别
资金
- RESCUE project
- SmartData@PoliTO research center
The European Union actively monitors wildfires through the Copernicus EMS Earth observation program to accelerate disaster response and decision-making processes. Research uses a state-of-the-art deep learning framework to analyze burned area severity estimation problems and proposes a multi-channel attention-based analysis approach.
Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.
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