Webinar: Geospatial Analysis Using CyVerse

Get a "bird's eye view" of available geospatial resources

Image
geospatial-desktop-ai

When

10 – 11 a.m., May 3, 2024
Webinar materials

Videorecording 

Presentation slides

About the webinar

Step out of conventional GIS frameworks and discover the latest trends in geospatial data science, where open tools, cloud technologies, and the proliferation of sensor data are innovating earth observation and environmental monitoring. Emphasizing open science and reproducible technologies, this webinar will provide an overview of the geospatial analyses and Cloud workflows that you can do in CyVerse.  Join us for the 'big picture view' of what is happening in geospatial data science and how you can use CyVerse resources to scale and share your science.

What you'll learn

  • The latest trends and technologies in geospatial data science
  • CyVerse resources available for geospatial analyses
  • Cloud workflows for geospatial analyses

About the presenter

Image
Jeff-gillan-headshot

Jeff Gillan is a Research Data Scientist at the University of Arizona Data Science Institute. In the research field, he provides cyberinfrastructure and data expertise for multiple research grants involving unmanned aerial systems. He is an educator who leads a variety of technical workshops on Open Science Skills, Cloud Native Geospatial, and AI Tools.

Jeff has 17 years of geospatial experience in the realms of applied research, government, and contracting. His core expertise is executing multi-scale imagery (drone, airplane, satellite) projects for land and resource investigations. He has extensive experience collecting, processing, and analyzing data for LiDAR, photogrammetry, multi-spectral, and hyperspectral workflows. He is specifically interested in ‘Cloud-Native’ geospatial to scale reproducible workflows and data sharing. Jeff holds a Ph.D. degree from the University of Arizona in Remote Sensing of Natural Resources.

 
 
 

Contacts

Create Account

An Open Science Workspace for Collaborative Data-driven Discovery