Empowering multi-source SAR Flood mapping with unsupervised learning

Flood mapping plays a crucial role in effective disaster management, risk assessment, and mitigation planning, given the widespread and destructive nature of floods. However, current synthetic aperture radar (SAR)-based methods face challenges related to extensive labeled training data, compromised...

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Vydáno v:Environmental research letters Ročník 20; číslo 1; s. 14006 - 14015
Hlavní autoři: Jiang, Xin, Zeng, Zhenzhong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.01.2025
Témata:
Adaptability
adaptability and scalability
Algorithms
Artificial intelligence
Artificial neural networks
Cloud computing
Disaster management
Emergency preparedness
Flood management
Flood mapping
Floods
global floods
high-performance cloud computing
Machine learning
Mapping
Mitigation
Neural networks
Risk assessment
SAR-based flood mapping
Satellites
Synthetic aperture radar
unsupervised algorithm
Unsupervised learning
ISSN:1748-9326, 1748-9326
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Shrnutí:Flood mapping plays a crucial role in effective disaster management, risk assessment, and mitigation planning, given the widespread and destructive nature of floods. However, current synthetic aperture radar (SAR)-based methods face challenges related to extensive labeled training data, compromised classification accuracy, and limited applicability across different satellite systems and resolutions. In response to these challenges, our research introduces a pioneering unsupervised SAR-based flood mapping algorithm, inspired by artificial general intelligence principles. Notably, the innovative method demonstrates flexibility, performing effectively across various SAR satellites with differing resolutions and sensors, eliminating the need for satellite-specific algorithms. Our algorithm enhances processing speed and scalability by eliminating labor-intensive labeling of training data and manual intervention. To validate its performance, we conducted tests in three distinct regions using meter-level imagery from HISEA-1, Gaofen-3, and Sentinel-1 satellites. Consistently outperformed prevalent unsupervised techniques like Kmeans and Otsu, and even a Supervised-convolutional neural network segmentation algorithm by AI-Earth, with F1-scores exceeding 0.91. This outstanding performance showcases its accuracy, irrespective of the satellite systems or regions utilized. Furthermore, the seamless integration of our algorithm with high-performance cloud computing platforms such as Google Earth Engine enhances its adaptability and scalability, enabling continuous monitoring of global floods. This is crucial in understanding flood trends, assessing their impacts, and formulating effective disaster mitigation strategies.
Bibliografie:ERL-117691.R2
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ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/ad9491