数据驱动的单灾种与多灾种易发性制图的演进及深度学习方法的兴起
Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods
AI总结：
本文探讨数据驱动的单灾种与多灾种易发性制图方法的演进，分析深度学习方法的兴起，提出将多模态深度学习数据融合策略应用于多灾种易发性制图的愿景，证明其潜力与应用空间。
摘要：
数据驱动的自然灾害易发性制图利用了分类方法的进展，这些方法应用于以栅格图像形式呈现的异质数据源。易发性制图是任何自然灾害风险评估的重要步骤。越来越多的灾害在空间、时间或两者上同时发生，这需要对多灾种易发性制图进行深入研究。近年来，单灾种易发性制图算法已趋成熟，并已扩展至多灾种易发性制图。深度学习也逐渐成为单灾种易发性制图的一种有前景的方法。本文讨论了单灾种方法的演进、其作为决策后期融合扩展至多灾种制图的情况，以及深度学习方法在易发性制图中的应用。最后，我们提出了将多模态深度学习中的数据融合策略应用于多灾种易发性制图的愿景。通过对易发性方法的背景研究，我们证明深度学习模型是多灾种易发性制图中具有潜力且尚未充分利用的方法，数据融合策略为适用于多灾种易发性制图的深度学习模型提供了更大的空间。
英文摘要：
Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented as raster images. Susceptibility mapping is an important step towards risk assessment for any natural hazard. Increasingly, multiple hazards co-occur spatially, temporally, or both, which calls for an in-depth study on multi-hazard susceptibility mapping. In recent years, single-hazard susceptibility mapping algorithms have become well-established and have been extended to multi-hazard susceptibility mapping. Deep learning is also emerging as a promising method for single-hazard susceptibility mapping. Here, we discuss the evolution of methods for a single hazard, their extensions to multi-hazard maps as a late fusion of decisions, and the use of deep learning methods in susceptibility mapping. We finally propose a vision for adapting data fusion strategies in multimodal deep learning to multi-hazard susceptibility mapping. From the background study of susceptibility methods, we demonstrate that deep learning models are promising, untapped methods for multi-hazard susceptibility mapping. Data fusion strategies provide a larger space of deep learning models applicable to multi-hazard susceptibility mapping.
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