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

本研究的目的是为冰盖遥感中心(CReSIS)收集的数据探索人工智能(AI)解决方案，以便提供智能的数据理解，以自动挖掘和分析CReSIS收集的异质数据集。已经并将花费大量资源从昂贵的北极和南极实地考察中收集和存储大型和不同种类的数据集(例如，通过NSF的大想法：导航新北极)。虽然传统分析提供了一些洞察力，但数据的复杂性、规模和多学科性质需要高级智能解决方案。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的调查，推动更广泛的大数据研究界。将特别关注妇女和少数族裔参与这项研究，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，冰顶和冰底的划定以及冰层内的分层对于监测冰盖和海冰的生长并建立模型至关重要。解决这一问题的最佳方法应该是将雷达探空仪数据与物理冰模型和相关数据集合并，如冰覆盖和集中图、时空气象图和冰速度。依赖数据的方法让机器学习这些关系，而不是直接将特定的关系工程到需要定义和调整许多参数的图像分析中。为了设计从北极和南极导航大数据的智能解决方案，并将当前和传统的技术扩大到大数据，该项目计划采用几种方法来探测冰面、底部、内层、基岩的三维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换到时频域。2)为机器配备人眼不可见或操作员难以同时考虑的信息，以便能够检测大范围的内部层和3D基础地形。利用雷达测高中冰面特征跟踪的结果，研究工作还将开发基于深度递归神经网络的依赖数据的新技术，用于预测未来几年的冰层厚度。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.