位置感知自适应归一化：一种用于野火危险预测的深度学习方法
Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting
AI总结：
本文提出一种带有位置感知自适应归一化层（LOAN）的2D/3D双分支CNN，通过基于地理位置调制动态特征并引入正弦日期编码，解决了野火预测中静态与动态变量处理及多分支特征割裂问题，在FireCube数据集上优于基线方法。
摘要：
气候变化预计将加剧并增加天气周期中的极端事件。由于这对我们生活的各个领域具有重大影响，近期的研究致力于从地球观测中识别和预测此类极端事件。在野火危险预测方面，以往的深度学习方法沿时间维度复制静态变量，忽略了静态变量与动态变量之间的内在差异。此外，现有的多数多分支架构在特征学习阶段丢失了分支之间的相互联系。为了解决这些问题，本文提出了一种带有位置感知自适应归一化层（LOAN）的 2D/3D 双分支卷积神经网络（CNN）。使用 LOAN 作为基础构建块，我们可以根据动态特征的地理位置对其进行调制。因此，我们的方法将特征属性视为一个统一且复合的 2D/3D 模型。此外，我们提出使用基于正弦的年中日期编码，为模型提供关于目标日在一年中的显式时间信息。实验结果表明，在具有挑战性的 FireCube 数据集上，我们的方法优于其他基线方法。结果表明，位置感知自适应特征归一化是一种很有前景的技术，可用于学习动态变量与其地理位置之间的关系，这对于以遥感数据作为分析基础的地区具有高度相关性。源代码可在 https://github.com/HakamShams/LOAN 获取。
英文摘要：
Climate change is expected to intensify and increase extreme events in the weather cycle. Since this has a significant impact on various sectors of our life, recent works are concerned with identifying and predicting such extreme events from Earth observations. With respect to wildfire danger forecasting, previous deep learning approaches duplicate static variables along the time dimension and neglect the intrinsic differences between static and dynamic variables. Furthermore, most existing multi-branch architectures lose the interconnections between the branches during the feature learning stage. To address these issues, this paper proposes a 2D/3D two-branch convolutional neural network (CNN) with a Location-aware Adaptive Normalization layer (LOAN). Using LOAN as a building block, we can modulate the dynamic features conditional on their geographical locations. Thus, our approach considers feature properties as a unified yet compound 2D/3D model. Besides, we propose using the sinusoidal-based encoding of the day of the year to provide the model with explicit temporal information about the target day within the year. Our experimental results show a better performance of our approach than other baselines on the challenging FireCube dataset. The results show that location-aware adaptive feature normalization is a promising technique to learn the relation between dynamic variables and their geographic locations, which is highly relevant for areas where remote sensing data builds the basis for analysis. The source code is available at https://github.com/HakamShams/LOAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
