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

合作研究：要素：数据：HDR：开发按需服务模块，用于从高分辨率图像中挖掘海冰的地球物理特性

• 批准号: 1835507
• 负责人: Chaowei Yang
• 金额: $ 24.93万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835507&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; Data; HDR

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.

海冰既是气候变化的指标，又是气候变化的放大器。目前，海冰观测数据有多种来源，可通过各种传感器网络（原位、机载和星载）获得。 通过开发用于分析海冰高空间分辨率（HSR）遥感图像的智能网络基础设施元素，科学界能够更好地提取用于气候建模的重要地球物理参数。该项目为海冰社区贡献了新的领域知识。 这是通过集成时空离散的 HSR 图像来实现的，以更快速、更可靠地识别冰类型，并通过标准化图像处理来创建兼容的海冰产品。 网络基础设施模块是一种增值的按需Web服务，可以与现有基础设施自然集成。关键目标是开发一个可靠、高效、符合开放地理空间联盟标准的按需Web服务，能够提取准确的地理信息。在有限的人为干预下，通过 HSR 图像了解水、水下冰、裸冰、融化的池塘、变形的“山脊”冰、山脊阴影以及其他信息。嵌入式时空分析框架提供了搜索、探索、可视化、组织和分析离散高铁影像以及其他相关遥感数据和现场数据的功能。该项目通过集成计算机视觉和机器学习算法、计算资源以及高铁图像数据和其他有用的数据集，为北极海冰社区创建数据和知识网络服务。概念模型改善了数据流，因此用户可以查询数据、下载增值数据，并在各种信息源中获得更一致的结果。 这为科学分析创造了新的机会，最大限度地减少处理复杂且时空离散的 HSR 图像的时间投入。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 教师提供夏季讲习班（由其他机构资助），重点强调下一代劳动力的教学和发展。收集到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共享。该奖项由高级网络基础设施办公室颁发，由 EarthCube 和极地项目办公室北极自然科学项目共同支持该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Environmental Data Collection for COVID-19 Pandemic Research

COVID-19 流行病研究的环境数据收集

• DOI: 10.3390/data5030068
• 发表时间: 2020-09
• 期刊: Data
• 影响因子: 2.6
• 作者: Liu, Qian;Liu, Wei;Sha, Dexuan;Kumar, Shubham;Chang, Emily;Arora, Vishakh;Lan, Hai;Li, Yun;Wang, Zifu;Zhang, Yadong;et al
• 通讯作者: et al

Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic

COVID-19大流行下美国医疗资源短缺的时空分析

• DOI: 10.1371/journal.pone.0240348
• 发表时间: 2020-10
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Sha, Dexuan;Miao, Xin;Lan, Hai;Stewart, Kathleen;Ruan, Shiyang;Tian, Yifei;Tian, Yuyang;Yang, Chaowei
• 通讯作者: Yang, Chaowei

A spatiotemporal data collection of viral cases for COVID-19 rapid response

用于 COVID-19 快速反应的病毒病例时空数据收集

• DOI: 10.1080/20964471.2020.1844934
• 发表时间: 2020-01
• 期刊: Big Earth Data
• 影响因子: 4
• 作者: Sha, Dexuan;Liu, Yi;Liu, Qian;Li, Yun;Tian, Yifei;Beaini, Fayez;Zhong, Cheng;Hu, Tao;Wang, Zifu;Lan, Hai;et al
• 通讯作者: et al

Challenges and opportunities of the spatiotemporal responses to the global pandemic of COVID-19

COVID-19全球大流行时空应对的挑战和机遇

• DOI: 10.1080/19475683.2022.2141396
• 发表时间: 2022-10
• 期刊: Annals of GIS
• 影响因子: 5
• 作者: Phil Yang, Chaowei;Bao, Shuming;Guan, Wendy;Howell, Kate;Hu, Tao;Lan, Hai;Li, Yun;Liu, Qian;Smith, Jennifer;Srirenganathan, Anusha;et al
• 通讯作者: et al

A State-Level Socioeconomic Data Collection of the United States for COVID-19 Research

美国用于 COVID-19 研究的州级社会经济数据收集

• DOI: 10.3390/data5040118
• 发表时间: 2020-12
• 期刊: Data
• 影响因子: 2.6
• 作者: Sha, Dexuan;Malarvizhi, Anusha Srirenganathan;Liu, Qian;Tian, Yifei;Zhou, You;Ruan, Shiyang;Dong, Rui;Carte, Kyla;Lan, Hai;Wang, Zifu;et al
• 通讯作者: et al