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| Storage: | The size of this resource is 40.3 MB | |
| Created: | Mar 30, 2026 at 1:19 a.m. (UTC) | |
| Last updated: | May 28, 2026 at 3:21 p.m. (UTC) (Metadata update) | |
| Published date: | May 28, 2026 at 3:21 p.m. (UTC) | |
| DOI: | 10.4211/hs.d0381011dbb044f3bd22aa7256ccb911 | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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Abstract
Validation evidence for the GRIME AI image triage module. Synthetic test images with known blur and brightness properties are processed through GRIME AI and results are compared against expected classifications. Includes validation of blur threshold detection, brightness lower and upper bound detection, and combined condition handling across six test cases. Contains the validation script, formal test plan, synthetic test images organized by test case, and CSV results from a validation run conducted on 2026-03-27
Subject Keywords
Content
README.md
GRIME AI Triage Validation
Overview
Validation evidence for the image triage module of the GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI) framework. This resource contains the validation script, test plan, synthetic test images organized by test case, and CSV results from a validation run conducted on 2026-03-27.
Contents
test_triage.py— Python script that generates the synthetic test images and implements the GRIME AI triage algorithm as of 2026-03-27GRIME_AI_Triage_Test_Plan - Rev 1.0 - 2026_03_27.docx— Formal test plandata/triage_test_images/— Synthetic test images organized by test case, generated by the validation scriptresults/— CSV outputs from GRIME AI triage run against the synthetic test images
Validation Approach
Synthetic test images with known blur and brightness properties are loaded into GRIME AI. GRIME AI triage output is compared against known expected classifications to confirm correct detection behavior across six test cases covering blur threshold, brightness lower bound, brightness upper bound, and combined conditions.
Cross-Validation
The validation script implements the same triage algorithm as GRIME AI as of 2026-03-27. Running the script against the synthetic test images provides an independent reference result that can be compared directly against GRIME AI output to cross-validate results.
Generating the Test Images
python test_triage.py generate
License
Apache License 2.0
Related Resources
GRIME AI source code: https://github.com/JohnStranzl/GRIME-AI
Additional Metadata
| Name | Value |
|---|---|
| License | Apache License 2.0 |
Related Resources
| This resource is described by | GRIME AI source code. Available at: https://github.com/JohnStranzl/GRIME-AI |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| U.S. National Science Foundation | Innovative Resources: Cyberinfrastructure and community to leverage ground-based imagery in ecohydrological studies | 2411065 |
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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