Collaborative Research: EarthCube Data Capabilities: Enabling analysis of heterogeneous, multi-source cryospheric data

协作研究：EarthCube 数据功能：实现异构、多源冰冻圈数据分析

• 批准号: 2026865
• 负责人: Farnoush Banaei-Kashani
• 金额: $ 25.18万
• 依托单位: University of Colorado at Denver-Downtown Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026865&HistoricalAwards=false
• 关键词: Collaborative; Research; EarthCube; Data; Capabilities

Sea ice is an important component of the climate system and an indicator of climate change. Sea ice data products are used in a variety of geosciences including physical and biological oceanography, climatology and meteorology. As a result of the combined effect of currents, winds, temperature fluctuations, and local and global climate patterns, sea ice is spatiotemporally dynamic, exhibiting a variety of evolving ice types that need classification for scientific analysis as well as operational planning for marine activities in the Arctic and Antarctic. The mapping and classification of sea ice, however, remains a scientific challenge, especially at high spatial and temporal resolutions. This project will build tools to make these data more readily accessible and lower barriers to the usage of federally funded data, especially by underrepresented researchers with less access to strong computational and/or educational resources. To ensure wide adoption, the project team will also develop related interactive tutorials and lab modules designed for students with little to no background in data science methods applicable to geoscience. In recent years, there has been a dramatic increase in the volume and variety of available data, due to both increases in the number of remote sensing instruments collecting data over the Arctic, growth in the number of models and the number of variables output by these models, creating an opportunity for high-resolution spatiotemporal sea ice mapping. The sheer volume and heterogeneity of such data pose a significant challenge to efficient and effective integration and analysis. The work will create modules for combining heterogeneous data products (e.g., satellite-borne passive microwave, SAR imagery from Sentinel-1, IceBridge, ICESat and ICESat-2 and the upcoming NISAR mission) and enable featurization of these heterogeneous data products using machine learning methods such as Restricted Boltzmann Machines and Deep Autoencoders to reduce the data to effectively represent the data while reducing size and dimensionality of the data. The project will build a modularized, semi-automatic, and interactive visual labeling platform utilizing the features generated by deep learning models for scalable labeled dataset creation, and integrate these products into the ecosystem of EarthCube services and user communities, and make the products available to the broader geoscience community.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.

海冰是气候系统的重要组成部分，也是气候变化的指标。海冰数据产品用于各种地球科学，包括物理和生物海洋学，气候学和气象学。由于电流，风，温度波动以及局部和全球气候模式的综合作用，海冰具有空间动态性，表现出各种不断发展的冰类型，需要分类进行科学分析，以及在北极和南极的海洋活动的运营计划。然而，海冰的映射和分类仍然是一项科学挑战，尤其是在高空间和时间分辨率下。 该项目将构建工具，使这些数据更容易访问，较低的障碍，尤其是由代表性不足的研究人员使用强大的计算和/或教育资源，尤其是代表性不足的研究人员。为了确保广泛的采用，项目团队还将开发相关的交互式教程和实验室模块，专为适用于地球科学的数据科学方法的学生而设计。近年来，由于遥感仪器的数量增加了北极的数据，模型数量的增长以及这些模型输出的变量数量增加，因此可用数据的数量和多种可用数据都大大增加，这为高分辨率时空时空海冰映射创造了机会。此类数据的纯粹体积和异质性对有效有效的整合和分析构成了重大挑战。 The work will create modules for combining heterogeneous data products (e.g., satellite-borne passive microwave, SAR imagery from Sentinel-1, IceBridge, ICESat and ICESat-2 and the upcoming NISAR mission) and enable featurization of these heterogeneous data products using machine learning methods such as Restricted Boltzmann Machines and Deep Autoencoders to reduce the data to effectively represent the data while reducing size和数据的维度。该项目将利用深度学习模型为可扩展标记的数据集创建而产生的功能建立一个模块化，半自动和互动的视觉标签平台，并将这些产品整合到EarthCube服务和用户社区的生态系统中，并使其对广泛的地理社区的广泛授权奖的构建奖，并使其能够提供了广泛的启发性。影响审查标准。