RIVET uses computer vision and water-quality sensors to investigate suspicious changes in waterways, verify evidence, and generate traceable environmental incident reports.
Inspiration
Environmental changes in waterways can go unnoticed when monitoring depends on isolated measurements or occasional inspections. We wanted to explore how computer vision and intelligent investigation could help identify suspicious changes and direct attention toward situations that need further examination.
What it does
RIVET (Environmental Incident Investigator) is a proposed system that combines OpenCV-based video analysis with water-quality sensor readings. It aims to detect unusual changes, compare visual evidence with sensor measurements, investigate uncertainty through additional analysis, and generate traceable incident reports.
How we built it
The planned prototype uses Python and OpenCV to analyse video over time. Water-quality sensors will provide independent measurements, while an AI agent will select follow-up analyses when observations are incomplete or conflicting. AWS will support the data-processing and storage workflow.
Challenges we expect
Important challenges include distinguishing genuine environmental changes from lighting variations, synchronising video with sensor readings, collecting reliable test data, and reducing false alarms.
Accomplishments we're aiming for
Our goal is to build a working prototype that demonstrates evidence-driven environmental investigation, combines computer vision with sensor measurements, and evaluates its performance against simpler monitoring methods.
What we aim to learn
We aim to investigate whether adaptive visual analysis and sensor fusion can improve environmental anomaly detection, how uncertainty should influence an AI agent's next action, and how to make automated environmental monitoring more transparent and reliable.
