Collaborative Proposal: Making Point Clouds Useful for Earth Science

爱达荷州立大学(ISU)、犹他州立大学(USU)和USFS落基山研究站(RMRS)的这一合作项目将为新的机载和地面激光雷达监测数据开发下一代分析和处理工具。这些工具将帮助地球科学家和自然资源管理者利用这些数据流来解决一些最紧迫的环境问题和管理挑战。这些软件工具将专门针对地球科学对来自机载激光雷达(ALS)、地面激光雷达(TLS)、多波束声纳(MBS)和/或运动结构(SFM)等平台的三维点云数据进行分析。该工具的开发将扩大现有记录和验证的算法，使其更易于使用，并构建应对新挑战所必需的新算法，例如重复测量的变化检测分析、改进的裸露地表生成以及从不同平台(例如ALS和TLS)收集的点云的新型融合。新软件将由地球科学家和计算机科学家合作开发，以优化处理与点云相关的大量且往往有问题的数据量时的性能。这项工作将提供一套社区软件，使创新和潜在的变革性数据分析成为可能，从而增强我们对众多地球表面过程的理解。

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.