信息基础设施
遥感
海冰
工作流程
北极的
地球观测
云计算
计算机科学
地球科学
地质学
数据科学
海洋学
数据库
工程类
卫星
航空航天工程
操作系统
作者
Dexuan Sha,Anusha Srirenganathan Malarvizhi,Hai Lan,Xin Miao,Hongie Xie,Daler Khamidov,Kevin Wang,Seren Smith,Katherine Howell,Chaowei Yang
出处
期刊:Geological Society of America eBooks
[Geological Society of America]
日期:2023-03-22
卷期号:: 71-84
标识
DOI:10.1130/2022.2558(06)
摘要
ABSTRACT The Arctic sea-ice region has become an increasingly important study area since it is not only a key driver of the Earth’s climate but also a sensitive indicator of climate change. Therefore, it is crucial to extract high-resolution geophysical features of sea ice from remote sensing data to model and validate sea-ice changes. With large volumes of high spatial resolution data and intensive feature extraction, classification, and analysis processes, cloud infrastructure solutions can support Earth science. One example is the Arctic CyberInfrastructure (ArcCI), which was built to address image management and processing for sea-ice studies. The ArcCI system employs an efficient geophysical feature extraction workflow that is based on the object-based image analysis (OBIA) method alongside an on-demand web service for Arctic cyberinfrastructure. By integrating machine learning classification approaches, the on-demand sea-ice high spatial resolution (HSR) imagery management and processing service and framework allows for the efficient and accurate extraction of geophysical features and the spatiotemporal analysis of sea-ice leads.
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