权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

IRES: Track II: The Coastal Processes & Machine Learning Advanced Studies Institute

IRES：轨道 II：沿海过程

基本信息

批准号：
1953412
负责人：
Evan Goldstein
金额：
$ 16.36万
依托单位：
University of North Carolina Greensboro
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953412&HistoricalAwards=false
关键词：
IRES Track II Coastal Processes

项目摘要

Coastlines host population centers, infrastructure, diverse ecosystems, and provide valuable tourism and recreational opportunities. Coastal regions are also prone to hazards such as storms, sea level rise, and chronic erosion. There is a growing volume of data available to scientists who study coastal processes and the future dynamics of coastlines. A range of new tools and methods that can be used to extract knowledge and insight from these data, especially machine learning techniques. However these new methods are not typically part of the graduate curriculum for coastal scientists. This project is focused on developing an advanced studies institute (ASI) in Auckland, New Zealand to teach machine learning methods to 20 US graduate students studying coastal processes and coastal geomorphology. The ASI participants are the next generation of US coastal scientists, who will get jobs in industry, agency, and academic settings.The ASI is taught by several scientists who focus on applying machine learning methods to coastal problems. The institute directly impacts 20 US graduate student participants, and provides a focused experience for them to develop machine learning skills in a coastal context. The ASI is designed to leverage resources that are unique to New Zealand, including multiple high-fidelity datasets. All course materials for the ASI will be built with open source software and stored in open repositories to facilitate use in other coastal teaching and learning settings beyond this ASI. A project evaluation will investigate the ASI learning goals (post-event and longitudinally). ASI outcomes and learning materials will be disseminated in print, online, and at scientific conferences.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.

旅游线路拥有人口中心、基础设施、多样化的生态系统，并提供宝贵的旅游和娱乐机会。沿海地区也容易受到风暴、海平面上升和慢性侵蚀等灾害的影响。研究海岸过程和海岸线未来动态的科学家可获得的数据越来越多。一系列新的工具和方法，可用于从这些数据中提取知识和见解，特别是机器学习技术。然而，这些新方法通常不是沿海科学家研究生课程的一部分。该项目的重点是在新西兰奥克兰建立一个高级研究所（ASI），向20名研究海岸过程和海岸地貌学的美国研究生教授机器学习方法。ASI的参与者是下一代美国海岸科学家，他们将在工业，机构和学术环境中找到工作。ASI由几位专注于将机器学习方法应用于海岸问题的科学家教授。该研究所直接影响了20名美国研究生参与者，并为他们提供了在沿海环境中发展机器学习技能的集中体验。ASI旨在利用新西兰独有的资源，包括多个高保真数据集。ASI的所有课程材料都将使用开放源代码软件构建，并存储在开放存储库中，以方便在ASI以外的其他沿海教学和学习环境中使用。项目评估将调查ASI学习目标（事后和纵向）。ASI成果和学习材料将以印刷品、在线和科学会议的形式传播。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

psi-collect: A Python module for post-storm image collection and cataloging

psi-collect：用于风暴后图像收集和编目的 Python 模块

DOI：
10.21105/joss.02075
发表时间：
2020
期刊：
Journal of Open Source Software
影响因子：
0
作者：
Moretz, Matthew;Foster, Daniel;Weber, John;Chowdhury, Rinty;Rafique, Shah;Goldstein, Evan;Mohanty, Somya
通讯作者：
Mohanty, Somya

An Active Learning Pipeline to Detect Hurricane Washover in Post-Storm Aerial Images

用于检测风暴后航空图像中飓风冲刷的主动学习管道