PlasticEye

Prototype simulation

PlasticEye

AI Satellite Early Warning System for Ocean Plastic Pollution

Detect plastic pollution before it reaches the ocean.

PlasticEye is a student-led AI climate project from Indonesia that turns satellite imagery and environmental data into cleanup-priority maps for coastal communities, NGOs, schools, and local governments.

Prototype Simulation Requires field verification Not live detection A demonstrative concept prototype, not a live operational satellite product.

Satellite Simulation Lab

Run a simulated AI scan on coastal and river-mouth plastic risk.

Select a location, compare the normal satellite-style view with the AI heatmap, and generate demonstrative output cards. Every result is labeled as a prototype simulation and requires field verification.

Bali Coastline, Indonesia Prototype satellite simulation view
Esri World Imagery satellite layer Sentinel-2 L2A Optical Layer Sentinel-1 SAR Backup AI Risk Heatmap: Prototype Simulation
Mission Telemetry SIM
Mission ID PLASTIC-EYE-48H
Layer Sentinel-2 L2A / Sentinel-1 SAR
Mode Prototype Simulation
Status AI Heatmap Ready
✓ Rainfall ✓ River Flow ✓ Drainage ✓ Population
T-48h Forecast · Cleanup Priority Alert
08.4095 S 115.1889 E Prototype Simulation Requires field verification Not live detection
Risk Legend
Red · Critical / High Risk
Yellow · Medium Risk
Green · Low Risk
Cyan · 48h Alert Zone
View framed · Prototype Simulation
Prototype Simulation · Not Live Detection

PlasticEye AI scan running

Preparing simulated data stack...

Prototype AI Guide

Explain the simulation like a field mission briefing.

The PlasticEye guide is a prototype assistant for judges, students, and community teams. It explains the model concept, the risk outputs, and why every hotspot still needs field verification.

What the guide can explain

  • How Sentinel-2 L2A and Sentinel-1 SAR support the simulation
  • Why rainfall, river flow, population density, and drainage matter
  • How to read risk score, confidence, forecast, and drivers
  • What field verification should happen before real action

How the AI Works

A competition-ready model concept that judges can understand fast.

PlasticEye is designed as a prototype workflow using satellite imagery, environmental risk layers, AI computer vision, and temporal forecasting.

Input Data

  • Sentinel-2 L2A optical imagery
  • Sentinel-1 SAR radar backup
  • Rainfall and river-flow data
  • Population density and drainage risk

AI Processing

  • Cloud filtering
  • Spectral pattern analysis
  • Computer vision hotspot detection
  • Temporal forecasting model
  • Risk score generation

Output

  • Plastic-risk heatmap
  • AI confidence score
  • 48-hour cleanup-priority alert
  • Field verification recommendation
Exact prototype flow: Sentinel-2 L2A + Sentinel-1 SAR + rainfall + river flow + population density → AI computer vision + temporal risk model → plastic hotspot heatmap → 48-hour cleanup-priority alert

Prototype Impact Targets

Built for local cleanup teams, schools, NGOs, and coastal communities.

70%Up to 70% reduction in manual survey time
55 pilot coastal / river-mouth zones
WeeklyWeekly cleanup-priority maps
48h48-hour early warning alerts
PlasticEye does not replace field verification. It helps communities know where to look first.

Future Model Architecture

Computer vision plus time-aware forecasting.

PlasticEye's future AI model combines CNN / U-Net image segmentation for satellite image patches with a Temporal Transformer or LSTM forecasting layer to learn pollution-risk changes over time. The model is designed to generate plastic-risk heatmaps and 48-hour cleanup-priority alerts.

Competition note

Prototype, not live detection.

This website simulates how PlasticEye could work. It does not claim to detect real plastic pollution from live satellite data today.

References and Data Framing

Grounded in credible climate and satellite sources.

UNICEF Venture Fund Frontier climate tech and children's health reference. UNICEF Climate change and environment for children. UNEP Plastic pollution context. Copernicus Sentinel-2 L2A optical imagery reference. Copernicus Sentinel-1 SAR radar reference. United Nations ActNow climate action campaign. FAO Waste reduction learning context.
Prototype simulation. Results are demonstrative and require field verification before real-world environmental action.