Honors Theses and Capstones

Date Completed

Spring 2026

Abstract

Estuary mudflats are ecologically sensitive environments that require consistent monitoring. Traditional satellite-based classification workflows are often constrained by the high cost and labor-intensive nature of manual data annotation. This study evaluates the utility of Segment Anything Model 3 (SAM 3), a foundational computer vision model, to automate mudflat segmentation without domain-specific fine-tuning. By leveraging the model’s text-prompting capabilities alongside specialized pre- and post-processing techniques, we generated segmentation masks in a zero-shot framework. Our approach achieved an F1- score of 0.51, demonstrating the inherent challenges of spectrally complex coastal features. Despite this, the results highlight a promising pathway for adapting large-scale foundational models to niche remote sensing tasks with minimal data overhead.

Document Type

Undergraduate Thesis

First Advisor

Samuel Carton

Second Advisor

Julie Paprocki

College or School

CEPS

Department or Program

Computer Science: Algorithms

Degree Name

Bachelor of Arts

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