Presenters-0667

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CSDMS 2025: Exploring Earth's Surface with Models, Data & AI


An Introduction to GRASS GIS and Tangible Landscape



Caitlin Haedrich

North Carolina State University, United States
chaedri@ncsu.edu
Pratikshya Regmi North Carolina State University United States


Abstract
This hands-on clinic will introduce participants to GRASS GIS, an open-source geospatial processing engine, and Tangible Landscape, a tangible user interface for GRASS GIS. We will explain and practice GRASS GIS concepts, and work through example Python-based workflows for topics such as hydrology, flood modeling, and viewshed analysis. These workflows will be implemented as a series of computational notebooks. Then, we will show how these workflows can be configured as activities on Tangible Landscape. Using GRASS GIS as a backend, Tangible Landscape is an interactive, open-source platform that integrates physical sand models of landscapes with digital simulations by using a scanner (xBox Kinect) and projector. It allows users to interact in real-time with models by, for example, carving the sand and seeing the resulting water flow pattern. By the end of the clinic, participants will have hands-on experience with:

- Setting up GRASS projects and importing data - Visibility analysis - Configuring and running overland flow models - Creating timeseries of inundation flooding

- Building Tangible Landscape activities

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Of interest for:
  • Terrestrial Working Group
  • Coastal Working Group
  • Marine Working Group
  • Education and Knowledge Transfer (EKT) Working Group
  • Cyberinformatics and Numerics Working Group
  • Hydrology Focus Research Group
  • Chesapeake Focus Research Group
  • Critical Zone Focus Research Group
  • Human Dimensions Focus Research Group
  • Geodynamics Focus Research Group
  • Ecosystem Dynamics Focus Research Group
  • Coastal Vulnerability Initiative
  • Continental Margin Initiative
  • Artificial Intelligence & Machine Learning Initiative
  • Modeling Platform Interoperability Initiative
  • River Network Modeling Initiative