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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Veröffentlicht in:	Environmental research letters Jg. 20; H. 1; S. 14006 - 14015
Hauptverfasser:	Jiang, Xin, Zeng, Zhenzhong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bristol IOP Publishing 01.01.2025
Schlagworte:	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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Abstract	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.
AbstractList	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.
Author	Jiang, Xin Zeng, Zhenzhong
Author_xml	– sequence: 1 givenname: Xin orcidid: 0000-0003-4141-1538 surname: Jiang fullname: Jiang, Xin organization: Southern University of Science and Technology School of Environmental Science and Engineering, Shenzhen 518055, People’s Republic of China – sequence: 2 givenname: Zhenzhong orcidid: 0000-0001-6851-2756 surname: Zeng fullname: Zeng, Zhenzhong organization: Eastern Institute of Technology Ningbo Institute of Digital Twin, Ningbo 315200, People’s Republic of China
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SubjectTerms	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
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Title	Empowering multi-source SAR Flood mapping with unsupervised learning
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