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The Year in Drone – 2024
Announcement | College of Natural Resources

The Year in Drone – 2024

As 2024 comes to a close, here’s a look back at some of the Drone Lab’s stats and projects.

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Post-processing Emlid GNSS data with OPUS corrections
Learning

Post-processing Emlid GNSS data with OPUS corrections

Aligning the photos to create an inital stereo model and sparse point cloud is the first step in Metashape. In this lab, we explore the different options for aligning photos and how to optimize the sparse point cloud.

Read More Post-processing Emlid GNSS data with OPUS correctionsContinue

Thermal Image Photogrammetry in Metashape
Learning

Thermal Image Photogrammetry in Metashape

Aligning the photos to create an inital stereo model and sparse point cloud is the first step in Metashape. In this lab, we explore the different options for aligning photos and how to optimize the sparse point cloud.

Read More Thermal Image Photogrammetry in MetashapeContinue

Combining Lidar with Photogrammetry in Metashape
Learning

Combining Lidar with Photogrammetry in Metashape

Aligning the photos to create an inital stereo model and sparse point cloud is the first step in Metashape. In this lab, we explore the different options for aligning photos and how to optimize the sparse point cloud.

Read More Combining Lidar with Photogrammetry in MetashapeContinue

Processing Lidar Data in Yellowscan CloudStation
Learning

Processing Lidar Data in Yellowscan CloudStation

Raw lidar data collected from Yellowscan sensors must be processed through CloudStation to produce high-accuracy point clouds and elevation models.

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Photo Alignment and Optimization in Metashape
Learning

Photo Alignment and Optimization in Metashape

Aligning the photos to create an inital stereo model and sparse point cloud is the first step in Metashape. In this lab, we explore the different options for aligning photos and how to optimize the sparse point cloud.

Read More Photo Alignment and Optimization in MetashapeContinue

Effects of Low-Tech Process-Based Restoration on Rangeland Riparian Areas
College of Natural Resources | Projects

Effects of Low-Tech Process-Based Restoration on Rangeland Riparian Areas

The University of Idaho’s Drone Lab has worked with the Montana Chapter of The Nature Conservancy to develop and refine workflows for automated processing of drone imagery into indicators of ecosystem health.

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Sam Fulbright
College of Natural Resources | Uncategorized

Sam Fulbright

Drone Lab Grad Student Sam Fulbright Graduate StudentDepartment of Forest, Rangeland, and Fire SciencesCollege of Natural Resources Sam’s Drone Lab Projects

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Eric Winford
College of Natural Resources | People

Eric Winford

Drone Lab Faculty Eric Winford, Ph.D. Research Assistant Professor of Rangeland EcologyAssociate Director, The Rangeland Center Dr. Winford’s Drone Lab Projects Dr. Winford’s Drone Publications

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Close-range Photogrammetry
Learning

Close-range Photogrammetry

Not drones, but still cool! You can use the same structure-from-motion software we use to process drone imagery to create 3D models from overlapping photographs of an object.

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Recent Posts

  • The Year in Drone – 2024
  • Post-processing Emlid GNSS data with OPUS corrections
  • Thermal Image Photogrammetry in Metashape
  • Combining Lidar with Photogrammetry in Metashape
  • Processing Lidar Data in Yellowscan CloudStation

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