ABSTRACT:

Validation evidence for the GRIME AI feature extraction module. Synthetic test images with known analytical properties are processed through GRIME AI and results are compared against expected values. Includes analytical validation of intensity, Shannon entropy, GCC, and ExG metrics using pure-color images, and directional validation of GLCM, Gabor, LBP, and Fourier descriptors using synthetic image pairs. Contains the validation script, formal test plan, synthetic test images, and CSV results from a validation run conducted on 2026-03-27.

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

ABSTRACT:

This collection holds sample datasets and work products from groups participating in the PEP2026 Workshop. Pixels to Enviro Patterns 2026 (PEP2026), is a two-day workshop at the University of Nebraska–Lincoln (Nebraska), where cameras, AI, and storytelling converge for scientific discovery. Held March 7–8 at Hardin Hall on East Campus, PEP 2026 invites researchers, educators, and creatives to explore open imagery and dive into hands-on learning with GRIME AI software. Collaborate on mini-projects to turn pixels into environmental patterns that drive scientific inquiry and narratives. Whether you’re passionate about water, phenology, artificial intelligence, or communication, this is your chance to connect, create, and be inspired.

Datasets are curated for use with GRIME AI software, which is available as a Conda package (https://anaconda.org/channels/GRIMELab/packages/grime-ai/overview), with source code and Wiki on GitHub (https://github.com/JohnStranzl/GRIME-AI/wiki). GRIME AI is free, open-source software (Apache 2.0). GRIME AI leverages Meta's Segment Anything 2 (SAM2) model for image segmentation and also facilitates the entire data science workflow, from data retrieval to model deployment.

This work was completed utilizing the Holland Computing Center of the University of Nebraska, which receives support from the UNL Office of Research and Innovation, and the Nebraska Research Initiative.