Collaborative Proposal: Making Point Clouds Useful for Earth Science

合作提案：使点云对地球科学有用

• 批准号: 1226145
• 负责人: Nancy Glenn
• 金额: $ 21.9万
• 依托单位: Idaho State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1226145&HistoricalAwards=false
• 关键词: Collaborative; Proposal; Making; Point; Clouds

This collaborative project between Idaho State University (ISU), Utah State University (USU), and USFS Rocky Mountain Research Station (RMRS) will develop the next generation of analytical and processing tools for new airborne and ground-based LiDAR monitoring data. The tools will help earth scientists and natural resource managers exploit these data streams to address some of our most pressing environmental questions and management challenges. The software tools will target Earth Science-specific analysis of 3-D point cloud data from platforms like airborne LiDAR (ALS), ground-based LiDAR (TLS), multi-beam SONAR (MBS), and/or Structure from Motion (SfM). The tool development will scale-up existing documented and proven algorithms to be more accessible, as well as build new algorithms that are necessary to address emerging challenges such as change detection analysis from repeat surveys, improved bare earth surface generation, and novel fusion of point clouds collected from different platforms (e.g. ALS and TLS). The new software will be developed collaboratively between geoscientists and computer scientists to optimize performance when handling the large and often problematic data volumes associated with point clouds. This work will provide a suite of community software that will permit innovative and potentially transformative data analysis with the potential to enhance our understanding of numerous earth surface processes.

爱达荷州立大学，犹他州立大学（USU）和USFS落基山研究站（RMRS）之间的合作项目将开发下一代的下一代分析和加工工具，用于新的机载和地面基于地面的LIDAR监测数据。这些工具将帮助地球科学家和自然资源经理利用这些数据流来解决我们一些最紧迫的环境问题和管理挑战。该软件工具将针对地球科学特定的分析，对平台，例如机载激光雷（ALS），基于地面的LIDAR（TLS），多光束声纳（MBS）（MBS）和/或结构（SFM）等平台。该工具开发将扩大现有的记录和经过证明的算法，以使其更容易访问，并构建新的算法，这些算法是应对新出现的挑战所必需的，例如重复调查，改进的裸露地面表面产生以及从不同平台收集的点云的新颖融合（例如，ALS和TLS）（例如，ALS和TLS）。新软件将在地球科学家和计算机科学家之间进行协作开发，以在处理与点云相关的大型且经常有问题的数据量时优化性能。这项工作将提供一套社区软件，该软件将允许创新和潜在的变革性数据分析，以增强我们对众多地面表面过程的理解。