Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods

TL;DR

This paper reviews the evolution of data-driven susceptibility mapping for natural hazards, highlighting the transition from single to multi-hazard approaches and the emerging role of deep learning and data fusion strategies.

Contribution

It provides a comprehensive overview of methods for single- and multi-hazard susceptibility mapping and proposes integrating deep learning with data fusion for improved multi-hazard risk assessment.

Findings

Deep learning shows promise for multi-hazard susceptibility mapping.

Multi-hazard mapping extends single-hazard methods through decision fusion.

Data fusion strategies expand the applicability of deep learning models.

Abstract

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.