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

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.