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

本研究的目的是探讨冰盖遥感中心（CReSIS）采集数据的人工智能解决方案，为CReSIS采集的异构数据集的自动挖掘和分析提供智能数据理解。大量资源已经并将用于收集和存储来自昂贵的北极和南极实地考察的大型异构数据集（例如通过NSF大构想：导航新北极）。虽然传统的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、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结合的方法，推进更广泛的大数据研究界。将特别关注妇女和少数民族参与研究，该项目将为西班牙裔和少数民族服务研究所的人工智能课程开发新的课程材料。在极地雷达测深成像中，冰顶和冰底以及冰内分层的描绘对于监测和模拟冰盖和海冰的生长是必不可少的。解决这一问题的最佳方法是将雷达测深数据与物理冰模型和相关数据集（如冰覆盖和浓度图、时空气象图和冰速）合并。而不是直接工程特定的关系到图像分析，需要许多参数来定义和调整，数据依赖的方法让机器学习这些关系。为实现北极和南极大数据导航的智能解决方案，将现有技术和传统技术推广到大数据，本项目规划了冰面、底部、内层探测、基岩三维建模和冰面时空监测的几种方法：1)设计基于混合网络的新方法，将机器学习与传统的特定领域知识相结合，将整个深度学习网络转换为时频域。2)为机器配备肉眼不可见或操作人员难以同时考虑的信息，能够大规模检测内层和三维基底地形。利用雷达测高中冰表面特征跟踪的结果，研究工作还将开发基于深度递归神经网络的新数据依赖技术，用于预测未来几年的冰厚度。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.