Collaborative Proposal: EarthCube RCN: Connecting the Earth Science and Cyberinfrastructure communities to advance the analysis of high resolution topography data

合作提案：EarthCube RCN：连接地球科学和网络基础设施社区，推进高分辨率地形数据的分析

• 批准号: 1642611
• 负责人: Paola Passalacqua
• 金额: $ 10.2万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642611&HistoricalAwards=false
• 关键词: Collaborative; Proposal; EarthCube; RCN; Connecting

This EarthCube Research Coordination Network (RCN) will bring together communities that create and use high resolution map data, including those that conduct research on earth surface processes and those that create the technology to make these types of complex data usable. Members of this network will work to share currently available resources and best practices, and to develop new tools to make data more available to researchers. Training will focus on teaching graduate student and early career researchers to access and use high resolution map data to answer earth science research questions. Vast quantities of High Resolution Terrain (HRT) data have been collected, with applications ranging from scientific research to commercial sector engineering. Full scientific utilization of these HRT data is still limited due to challenges associated with the storage, manipulation, processing, and analysis of these data. The cyberinfrastructure community, including computer vision, computer science, informatics, and related engineering fields are developing advanced tools for visualizing, cataloging, and classifying imagery data including point clouds. Yet, many of these tools are most applicable to engineered structures and small datasets, and not to heterogeneous landscapes. Together the earth science and cyberinfrastructure communities have the opportunity to test and validate emerging tools in challenging landscapes (e.g., heterogeneous and multiscale landforms, vegetation structures, urban footprints). In particular, this RCN will be focused on four themes: (1) coordination of the analysis of HRT data across the earth surface processes and hydrology communities to identify work-flows and best practices for data analysis; (2) identification of cyberinfrastructure tool development needs as new technologies for HRT data acquisition emerge; (3) use of HRT data for numerical models validation and integration of HRT data information in models; (4) training in HRT best practices, and data processing and analysis work-flows.

这个地球立方体研究协调网络（RCN）将汇集创建和使用高分辨率地图数据的社区，包括那些对地球表面过程进行研究的社区和那些创建技术使这些类型的复杂数据可用的社区。该网络的成员将努力分享现有的资源和最佳做法，并开发新的工具，使研究人员更容易获得数据。培训将侧重于教授研究生和早期职业研究人员访问和使用高分辨率地图数据来回答地球科学研究问题。已经收集了大量的高分辨率地形（HRT）数据，其应用范围从科学研究到商业部门工程。由于与这些数据的存储、操作、处理和分析相关的挑战，这些HRT数据的充分科学利用仍然有限。包括计算机视觉、计算机科学、信息学和相关工程领域在内的网络基础设施社区正在开发用于可视化、编目和分类包括点云在内的图像数据的高级工具。然而，其中许多工具最适用于工程结构和小型数据集，而不是异构景观。地球科学和网络基础设施社区有机会在具有挑战性的景观中测试和验证新兴工具（例如，异质和多尺度地貌、植被结构、城市足迹）。特别是，该RCN将侧重于四个主题：（1）协调地球表面过程和水文社区的HRT数据分析，以确定数据分析的工作流程和最佳实践;（2）随着HRT数据采集新技术的出现，确定网络基础设施工具开发需求;（3）使用HRT数据进行数值模型验证和模型中HRT数据信息的整合;（4）HRT最佳实践培训，以及数据处理和分析工作流程。