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

2025-02-13

AI Summary

This paper addresses the low accuracy and inefficiency of single-hazard and multi-hazard (spatiotemporally coupled) susceptibility mapping. We propose the first multimodal deep learning framework specifically designed for joint multi-hazard modeling. Methodologically, it departs from conventional late-fusion paradigms by introducing feature-level and decision-level joint modeling of heterogeneous raster data—including remote sensing imagery and geoenvironmental covariates—and integrates convolutional neural networks with a novel multi-branch feature fusion mechanism. Experimental results demonstrate that the framework significantly outperforms traditional models in single-hazard susceptibility mapping; moreover, it achieves, for the first time, high-accuracy quantitative joint susceptibility estimation under multi-hazard scenarios, validating its strong generalizability and scalability. This work establishes both theoretical foundations and methodological frameworks for developing unified, intelligent, and transferable hazard risk mapping paradigms.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Evolution of data-driven susceptibility mapping methods.
• Extension to multi-hazard susceptibility mapping challenges.
• Emergence of deep learning in hazard mapping.

Innovation

• Deep learning for hazard mapping
• Data fusion in multi-hazard analysis
• Evolution from single to multi-hazard methods