BIGDATA: IA: Collaborative Research: Intelligent Solutions for Navigating Big Data from the Arctic and Antarctic

BIGDATA：IA：协作研究：导航北极和南极大数据的智能解决方案

• 批准号: 1838230
• 负责人: Maryam Rahnemoonfar
• 金额: $ 61.28万
• 依托单位: Texas A&M University Corpus Christi
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838230&HistoricalAwards=false
• 关键词: BIGDATA; IA; Collaborative; Research; Intelligent

The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS. Significant resources have been and will be spent in collecting and storing large and heterogeneous datasets from expensive Arctic and Antarctic fieldwork (e.g. through NSF Big Idea: Navigating the New Arctic). While traditional analyses provide some insight, the complexity, scale, and multidisciplinary nature of the data necessitate advanced intelligent solutions. This project will allow domain scientists to automatically answer questions about the properties of the data, including ice thickness, ice surface, ice bottom, internal layers, ice thickness prediction, and bedrock visualization. The planned approach will advance the broader big data research community by improving the efficiency of deep learning methods and in the investigation of methods to merge data-driven AI approaches with application-specific domain knowledge. Special attention will be given to women and minority involvement in the research and the project will develop new course materials for several classes in AI at a Hispanic and minority serving institute.In polar radar sounder imagery, the delineation of the ice top and ice bottom and layering within the ice is essential for monitoring and modeling the growth of ice sheets and sea ice. The optimal approach to this problem should merge the radar sounder data with physical ice models and related datasets such as ice coverage and concentration maps, spatiotemporal meteorological maps, and ice velocity. Rather than directly engineering specific relations into the image analysis that require many parameters to be defined and tuned, data-dependent approaches let the machine learn these relationships. To devise intelligent solutions for navigating the big data from the Arctic and Antarctic and to scale up the current and traditional techniques to big data, this project plans several approaches for detecting ice surface, bottom, internal layers, 3D modeling of bedrock and spatial-temporal monitoring of the ice surface: 1) Devise new methodologies based on hybrid networks combining machine learning with traditional domain specific knowledge and transforming the entire deep learning network to the time-frequency domain. 2) Equip the machine with information that is not visible to the human eye or that is hard for a human operator to consider simultaneously, to be able to detect internal layers and 3D basal topography on a large scale. Using the results of the feature tracking of the ice surface in radar altimetry, the research effort will also develop new data-dependent techniques for predicting the ice thickness for following years based on deep recurrent neural networks.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.

本研究的目的是研究冰盖遥感中心 (CReSIS) 收集的数据的人工智能 (AI) 解决方案，以便提供智能数据理解，以自动挖掘和分析 CReSIS 收集的异构数据集。已经并将花费大量资源来收集和存储来自昂贵的北极和南极实地工作的大型异构数据集（例如通过 NSF Big Idea：导航新北极）。虽然传统分析提供了一些见解，但数据的复杂性、规模和多学科性质需要先进的智能解决方案。该项目将允许领域科学家自动回答有关数据属性的问题，包括冰厚度、冰表面、冰底部、内层、冰厚度预测和基岩可视化。计划中的方法将通过提高深度学习方法的效率以及研究将数据驱动的人工智能方法与特定应用领域知识相融合的方法来推动更广泛的大数据研究社区的发展。将特别关注妇女和少数族裔参与研究，该项目将为西班牙裔和少数族裔服务机构的几个人工智能课程开发新的课程材料。在极地雷达探测仪图像中，冰顶和冰底的描绘以及冰内的分层对于监测和模拟冰盖和海冰的生长至关重要。解决这个问题的最佳方法应该将雷达探测仪数据与物理冰模型和相关数据集（例如冰覆盖范围和浓度图、时空气象图和冰速度）合并。数据依赖的方法不是直接将特定关系设计到需要定义和调整许多参数的图像分析中，而是让机器学习这些关系。为了设计导航北极和南极大数据的智能解决方案，并将当前和传统技术扩展到大数据，该项目计划采用多种方法来检测冰面、底部、内部层、基岩和时空的 3D 建模冰面监测：1）设计基于混合网络的新方法，将机器学习与传统领域特定知识相结合，并将整个深度学习网络转换到时频域。 2）为机器配备人眼不可见或人类操作员难以同时考虑的信息，以能够大范围检测内层和3D基底形貌。利用雷达测高中冰面特征跟踪的结果，该研究工作还将开发新的数据依赖技术，用于基于深度循环神经网络预测未来几年的冰厚度。该奖项反映了 NSF 的法定使命，并已被通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。