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

基本信息

批准号：
1947584
负责人：
Maryam Rahnemoonfar
金额：
$ 58.93万
依托单位：
University of Maryland Baltimore County
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947584&HistoricalAwards=false
关键词：
BIGDATA IA Collaborative Research Intelligent

项目摘要

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.

这项研究的目的是研究由冰盖遥感中心（CRESAS）收集的数据的人工智能（AI）解决方案，以提供智能数据理解，以自动挖掘并分析CRESAS收集的异质数据集。从昂贵的北极和南极实地调查中收集和存储大型和异质数据集（例如，通过NSF Big Ideas：浏览新北极），将花费大量资源。尽管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特性的问题，包括冰层，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对数据驱动的AI方法与应用特定的领域知识合并的方法来提高更广泛的大数据研究界。将特别关注妇女和少数参与研究的参与，该项目将在西班牙裔和少数派服务学院为AI的多个类别开发新课程材料。在极地雷达音响器图像中，冰层和冰底部的划定以及冰中的分层对于监测和模拟了冰块和海冰的生长至关重要。解决此问题的最佳方法应将雷达声音数据与物理冰模型和相关数据集合并，例如冰覆盖和浓度图，时空气象图和冰速度。与其直接在图像分析中直接设计特定的关系，该关系需要定义和调整许多参数，而是让机器学习这些关系。为了设计智能解决方案，以从北极和南极导航大数据，并扩大当前和传统的技术到大数据的最新技术，该项目计划了几种方法，用于检测冰面，底部，内部，内部，内部，3D模型，底层建模和空间 - 周期性的模型，以及基于冰层的深入研究，以将新的方法进行组合：1）时频域。 2）为机器配备人眼看不到的信息，或者人类操作员很难同时考虑，能够大规模检测内部层和3D基础形态。利用雷达高度测定中冰表面的特征跟踪的结果，研究工作还将开发新的与数据相关的技术，以根据深层复发的神经网络来预测接下来几年的冰厚度。这项奖项反映了NSF的法定任务，并已通过评估该基金会的知识功能和广泛的影响来评估NSF的法定任务，并被认为是值得的。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

DOI：
10.3390/s19245479
发表时间：
2019-12
期刊：
Sensors (Basel, Switzerland)
影响因子：
0
作者：
M. Rahnemoonfar;Jimmy Johnson;J. Paden
通讯作者：
M. Rahnemoonfar;Jimmy Johnson;J. Paden

Refining Ice Layer Tracking through Wavelet combined Neural Networks

通过小波组合神经网络完善冰层跟踪

DOI：
发表时间：
2021
期刊：
2021
影响因子：
0
作者：
Debvrat Varshney, Masoud Yari
通讯作者：
Debvrat Varshney, Masoud Yari

Multi-Scale and Temporal Transfer Learning for Automatic Tracking of Internal Ice Layers

DOI：
10.1109/igarss39084.2020.9323758
发表时间：
2020-09
期刊：
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
影响因子：
0
作者：
M. Yari;M. Rahnemoonfar;J. Paden
通讯作者：
M. Yari;M. Rahnemoonfar;J. Paden

Smart Tracking of Internal Layers of Ice in Radar Data via Multi-Scale Learning

通过多尺度学习智能跟踪雷达数据中的冰内层

DOI：
10.1109/bigdata47090.2019.9006083
发表时间：
2019
期刊：
2019 IEEE International Conference on Big Data (Big Data
影响因子：
0
作者：
Yari, Masoud;Rahnemoonfar, Maryam;Paden, John;Oluwanisola, Ibikunle;Koenig, Lora;Montgomery, Lynn
通讯作者：
Montgomery, Lynn

Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet