A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping

TL;DR

This survey reviews deep learning techniques for 3D flood mapping, highlighting advancements, data sources, applications, challenges, and future directions to improve disaster management and urban planning.

Contribution

It provides a comprehensive categorization and comparison of DL architectures for 3D flood mapping, and discusses data sources, applications, and future research challenges.

Findings

Deep learning enhances 3D flood prediction accuracy.

Integration of diverse data sources improves flood mapping.

Challenges include data scarcity and model interpretability.

Abstract

Flooding remains a major global challenge, worsened by climate change and urbanization, demanding advanced solutions for effective disaster management. While traditional 2D flood mapping techniques provide limited insights, 3D flood mapping, powered by deep learning (DL), offers enhanced capabilities by integrating flood extent and depth. This paper presents a comprehensive survey of deep learning-based 3D flood mapping, emphasizing its advancements over 2D maps by integrating flood extent and depth for effective disaster management and urban planning. The survey categorizes deep learning techniques into task decomposition and end-to-end approaches, applicable to both static and dynamic flood features. We compare key DL architectures, highlighting their respective roles in enhancing prediction accuracy and computational efficiency. Additionally, this work explores diverse data sources such as digital elevation models, satellite imagery, rainfall, and simulated data, outlining their roles in 3D flood mapping. The applications reviewed range from real-time flood prediction to long-term urban planning and risk assessment. However, significant challenges persist, including data scarcity, model interpretability, and integration with traditional hydrodynamic models. This survey concludes by suggesting future directions to address these limitations, focusing on enhanced datasets, improved models, and policy implications for flood management. This survey aims to guide researchers and practitioners in leveraging DL techniques for more robust and reliable 3D flood mapping, fostering improved flood management strategies.