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| Type: | Resource | |
| Storage: | The size of this resource is 15.0 MB | |
| Created: | Mar 10, 2026 at 11:23 p.m. (UTC) | |
| Last updated: | Mar 10, 2026 at 11:58 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
This resource is the end product of my project work at the Pixels to Enviro Patterns 2026 workshop, hosted at the University of Nebraska - Lincoln.
For this project, I extracted 3 images per day from 09-05-2025 to 02-20-2026 from GRIME-AI at the USGS site CO Eagle River near Minturn. Using the CVAT annotation tool, I then annotated 15 images spread out at even intervals across that time period for the presence of water, earth, sky, snow, and human infrastructure. I attempted to train GRIME-AI to detect the presence of water in the river by using this annotated set of data to train a water detection model. Finally, I applied the model to the entire initial set of images I extracted and used GRIME-AI's image segmentation tool to produce masks identifying where water was present in each image.
The resource contains all fifteen images I used to train the model with, the file containing my annotations, and a selection of masks demonstrating the output of the model.
This material is based in part upon work supported by the United States Geological Survey.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Related Resources
| Title | Owners | Sharing Status | My Permission |
|---|---|---|---|
| PEP2026: GRIME AI Data and Products for the Pixels to Environmental Patterns Workshop | Troy Gilmore · Nawaraj Shrestha · John Stranzl · Zach Nickerson | Public & Shareable | Open Access |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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