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We use ground-based time-lapse imagery for ecohydrological research, education and communication.

stream monitoring site with weir and GaugeCam and camera monitoring system
Kearney Outdoor Learning Area stream monitoring site with downed tree across a gravel-bed creek
Sandhills groundwater-fed stream with monitoring equipment installed
Scientists setting up monitoring equipment on a groundwater-fed Sandhills stream in Nebraska
Stop sign target for GaugeCam image-based water level measurement system
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Month: June 2025

Water Segmentation for Trail Cam Imagery

Posted on June 6, 2025 by Troy

Here’s one example test image from the Kearney Outdoor Learning Area (KOLA). This segmentation model was tuned as part of our USGS-funded project looking at 11 HIVIS sites.

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GRIME AI Wiki

Posted on June 6, 2025 by Troy

We have a GRIME AI Wiki! Check it out at https://github.com/JohnStranzl/GRIME-AI/wiki

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Who we are:

Troy Gilmore – hydrology – Associate Professor, Conservation and Survey Division – School of Natural Resources, University of Nebraska-Lincoln

Ken Chapman – programming and machine vision, pursuing PhD in Natural Resources Sciences, Conservation and Survey Division – School of Natural Resources, University of Nebraska-Lincoln

John Stranzl – software development and machine learning, pursuing PhD in Natural Resources Sciences, Conservation and Survey Division – School of Natural Resources, University of Nebraska-Lincoln

Mary Harner – ecosystem, riparian, and wetland ecology; science communication – Research Professor, Department of Communication, Department of Biology, University of Nebraska at Kearney

François Birgand – ecological engineering, Professor, Biological and Agricultural Engineering – North Carolina State University

Mehrube Mehrubeoglu – machine learning and image analysis – Texas A&M University – Corpus Christi

Christian Chapman – Statistical signal processing and mathematical analysis – Technical Staff, MIT Lincoln Laboratory. 

 

 

Who we work with:

We are grateful for excellent colleagues at the University of Nebraska, including access to the incredible imagery of:

  • Platte Basin Timelapse Project (Mike Forsberg, School of Natural Resources, University of Nebraska-Lincoln)

GaugeCam users/testers:

  • ITESM (Guadalajara Campus and Puebla Campus)
    • GRIME-derived data has been used in two computer science courses (since Fall 2022)
      • Advanced AI for Data Science (1)
      • Business Solution Development Capstone projects (1, 2 and 3) 
    • Developing methods for monitoring river pollution
  • Idaho Power
    • field test completed at Boise State University Campus (spring 2021)
  • University of Kansas (Konza Prairie field application started in 2021)
  • University of Texas – Arlington (Sanchez-Murillo Lab)

GaugeCam Blog:

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Water Segmentation for Trail Cam Imagery

Troy June 6, 2025

Here’s one example test image from the Kearney Outdoor Learning Area (KOLA). This segmentation model was tuned as part of […]

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GRIME AI Wiki

Troy June 6, 2025

We have a GRIME AI Wiki! Check it out at https://github.com/JohnStranzl/GRIME-AI/wiki

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*New* GRIME2 User Guide

Troy November 2, 2024

This week we created a basic user guide for GRIME2. Our next step is to work through some bugs we […]

Our MESH Software Development Philosophy

Apply Findable, Accessible, Interoperable, and Repeatable (FAIR) principles, to:

  • Maximize use and value of image archives
  • Expedite data triage and generation of hypotheses
  • Shift the time investment to the scientific questions
  • High usability and modular design to include non-(expert) programmers in science and education

GRIME2 and GRIME AI are both released using the Apache 2.0 license

Funding

We gratefully acknowledge funding from the following entities:

Daugherty Water for Food Global Institute (Graduate Fellowship)

John C. and Nettie V. David Memorial Trust Fund

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U.S. Geological Survey Logo

This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G23AC00141-00. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.

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GRIME AI development, related data products, and training resources are partially supported by the National Science Foundation.

Copyright © 2020-2025 Troy E. Gilmore. All rights reserved.