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

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

URL: http://arxiv.org/abs/2506.13201v1
Date: Mon, 16 Jun 2025 08:06:18 GMT
Title: A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping
Authors: Wenfeng Jia, Bin Liang, Yuxi Liu, Muhammad Arif Khan, Lihong Zheng,
Abstract summary: Flooding remains a major global challenge, worsened by climate change and urbanization, demanding advanced solutions for effective disaster management.<n>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.<n>This paper presents a comprehensive survey of deep learning-based 3D flood mapping, emphasizing its advancements over 2D maps.
Score: 6.088214521097484
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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