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
Online-Zugang:	Volltext
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