CyberTraining: Implementation: Medium: Machine Learning Training and Curriculum Development for Earth Science Studies

网络培训：实施：媒介：地球科学研究的机器学习培训和课程开发

• 批准号: 2117834
• 负责人: Nicoleta Cristea
• 金额: $ 99.58万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117834&HistoricalAwards=false
• 关键词: CyberTraining; Implementation; Medium; Machine; Learning

Earth system science discoveries are increasingly affected by data management, analysis, and inference using powerful machine learning (ML) techniques. Yet, the skills required to perform these tasks, and training in cutting-edge, open-source technologies to build ML models and pipelines, big data, and cloud computing, are not covered by the traditional graduate curriculum in the geosciences. To fill these gaps, this project will develop the GeoScience MAchine Learning Resources and Training (GeoSMART) framework that will build a foundation in open-source scientific ecosystems and general ML theory, toolkits, and deployment on Cloud computing platforms. This project will include a team of geoscience and ML educators to create a novel ML curriculum with focus on seismology, cryosphere and hydrology applications. The training materials will be included in an enhanced curriculum that will broaden impact on emerging ML communities. The project’s implementation plan will provide training in open-source ML toolkits and data science skills. Further, the project will cultivate the development of discipline-specific ML libraries, workflows, and communities of practice to sustain future growth of ML cybertraining opportunities. By building tools using open-source and cloud-accessible platforms, and by partnering with colleges and institutions that lack computing resources for ML workflows, the project will increase access to cybertraining materials and help to solve geoscience challenges.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.

地球系统科学发现越来越多地受到使用强大的机器学习（ML）技术进行数据管理、分析和推理的影响。然而，执行这些任务所需的技能，以及构建ML模型和管道，大数据和云计算的尖端开源技术培训，并不包括在地球科学的传统研究生课程中。为了填补这些空白，该项目将开发地球科学机器学习资源和培训（GeoSMART）框架，该框架将在开源科学生态系统和一般ML理论，工具包以及云计算平台上的部署中奠定基础。 该项目将包括一个地球科学和ML教育工作者团队，以创建一个新的ML课程，重点是地震学，冰冻圈和水文应用。这些培训材料将被纳入增强型课程，以扩大对新兴ML社区的影响。该项目的实施计划将提供开源ML工具包和数据科学技能的培训。此外，该项目将培养特定学科的ML库，工作流程和实践社区的发展，以维持ML网络培训机会的未来增长。通过使用开源和云访问平台构建工具，并与缺乏ML工作流程计算资源的学院和机构合作，该项目将增加网络培训材料的访问权限，并帮助解决地球科学挑战。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。

项目成果

GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation

GStatSim V1.0：用于地统计插值和条件模拟的 Python 包

• DOI: 10.5194/gmd-16-3765-2023
• 发表时间: 2023
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: MacKie, Emma J.;Field, Michael;Wang, Lijing;Yin, Zhen;Schoedl, Nathan;Hibbs, Matthew;Zhang, Allan
• 通讯作者: Zhang, Allan

High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery

• DOI: 10.3390/rs14143409
• 发表时间: 2022-07
• 期刊: Remote. Sens.
• 作者: Aji John;A. Cannistra;Kehan Yang;Amanda Tan;D. Shean;J. R. Lambers;N. Cristea

High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning

• DOI: 10.3389/frwa.2023.1128758
• 发表时间: 2023-06
• 作者: Kehan Yang;Aji John;D. Shean;J. Lundquist;Ziheng Sun;Fangfang Yao;Stefan Todoran;N. Cristea