Elements: Data: HDR: Collaborative Research: Developing an On-Demand Service Module for Mining Geophysical Properties of Sea Ice from High Spatial Resolution Imagery

海冰既是气候变化的指示器，也是气候变化的放大器。目前，海冰观测有多种来源，可从各种传感器网络（现场、机载和星载）获得。 通过开发智能网络基础设施元素，用于分析海冰上的高空间分辨率（HSR）遥感图像，科学界能够更好地提取重要的地球物理参数，用于气候建模。该项目为海冰社区贡献了新的领域知识。 这是通过整合时空离散的HSR图像来实现的，以产生更快速和可靠的冰类型识别，并通过标准化的图像处理来创建兼容的海冰产品。 网络基础设施模块是一个增值的按需网络服务，可以与现有的基础设施自然集成。主要目标是开发一个可靠和高效的按需开放地理空间联盟兼容的网络服务，该服务能够提取水，潜冰，裸冰，融化的池塘，变形的“脊”冰，山脊阴影，和其他信息从HSR图像与有限的人为干预。嵌入式时空分析框架提供了搜索、探索、可视化、组织和分析离散HSR图像以及其他相关遥感数据和现场数据的功能。该项目通过整合计算机视觉和机器学习算法、计算资源、HSR图像数据和其他有用的数据集，为北极海冰社区创建了一个数据和知识网络服务。概念模型改进了数据流，因此用户可以查询数据，下载增值数据，并在各种信息源之间获得更一致的结果。 这为科学分析创造了新的机会，最大限度地减少了处理复杂和时空离散HSR图像的时间投资。该项目包括通过课程开发，研究生和本科生参与研究，以及为K-12教师提供暑期讲习班（由其他机构资助），大力强调下一代劳动力的教学和发展。收集的图像和图像分析结果将通过NSF北极数据中心及时与公众分享。高级网络基础设施办公室的这一奖项由EarthCube和极地计划办公室北极自然科学计划共同支持，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响评审标准。

Sea ice acts as both an indicator and an amplifier of climate change. At present, there are multiple sources of sea ice observations which are obtained from a variety of networks of sensors (in situ, airborne, and space-borne). By developing a smart cyberinfrastructure element for the analysis of high spatial resolution (HSR) remote sensing images over sea ice, the science community is better able to extract important geophysical parameters for climate modeling. The project contributes new domain knowledge to the sea ice community. This is accomplished by integrating HSR images that are spatiotemporally discrete to produce a more rapid and reliable identification of ice types, and by a standardized image processing that allows creating compatible sea ice products. The cyberinfrastructure module is a value-added on-demand web service that can be naturally integrated with existing infrastructure.The key objective is to develop a reliable and efficient on-demand Open Geospatial Consortium-compliant web service, which is capable of extracting accurate geographic knowledge of water, submerged ice, bare ice, melt ponds, deformed 'ridging' ice, ridge shadows, and other information from HSR images with limited human intervention. The embedded spatial-temporal analysis framework provides functions to search, explore, visualize, organize, and analyze the discrete HSR images and other related remote sensing data and field data. The project creates a data and knowledge web service for the Arctic sea ice community by integrating computer vision and machine learning algorithms, computing resources, and HSR image data and other useful datasets. The conceptual model improves data flow, so users would query data, download value-added data, and have more consistent results across various sources of information. This creates new opportunities for scientific analysis that minimizes the investment of time in processing complex and spatiotemporally-discrete HSR imagery. The project includes a strong emphasis on teaching and development of the next-generation workforce through course curricula development, involvement of graduate and undergraduate students in research, and the offering of summer workshops for K-12 teachers (funded by other agencies). The collected images and results of the image analyses will be shared with the public in a timely manner through the NSF Arctic Data Center.This award by the Office of Advanced Cyberinfrastructure is jointly supported by EarthCube and the Office of the Polar Programs Arctic Natural Sciences Program, within the NSF Directorate for Geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.