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

本研究的目的是研究人工智能（AI）解决方案，由冰盖遥感中心（CReSIS）收集的数据，以提供智能数据理解，自动挖掘和分析CReSIS收集的异构数据集。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 Big Idea：Navigating the New Arctic）。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，冰面，冰底，内部层，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于应用的领域知识相结合的方法来推动更广泛的大数据研究社区。该项目将特别关注妇女和少数民族参与研究，并将为西班牙裔和少数民族服务机构的几个人工智能课程开发新的课程材料。在极地雷达探测器图像中，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是将雷达探测器数据与物理冰模型和相关数据集（如冰覆盖率和浓度图、时空气象图和冰速度）相结合。与其直接将特定的关系设计到需要定义和调整许多参数的图像分析中，依赖数据的方法让机器学习这些关系。为了设计智能解决方案来导航来自北极和南极的大数据，并将当前和传统技术扩展到大数据，该项目计划采用几种方法来检测冰面，底部，内部层，基岩的3D建模和冰面的时空监测：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，并将整个深度学习网络转变为时间-频域2)为机器配备人眼不可见或操作人员难以同时考虑的信息，以便能够大规模检测内部层和3D基底形貌。利用雷达测高中冰面特征跟踪的结果，研究工作还将开发新的数据依赖技术，用于基于深度递归神经网络预测未来几年的冰厚。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。

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.