Ghost Hunter

Detecting dark vessels in protected oceans

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🌊 Ghost Hunter: Project Story

🚀 About the Project

Ghost Hunter is an AI-driven maritime intelligence system designed to detect “dark vessels”—ships that deliberately turn off their AIS (Automatic Identification System) to avoid tracking—within Marine Protected Areas (MPAs). These vessels pose a serious threat to marine ecosystems through illegal fishing and unauthorized activity.

The core idea behind Ghost Hunter is simple yet powerful:
👉 If vessels try to hide digitally, we detect them physically.

💡 What Inspired Us

Oceans cover more than 70% of the Earth, yet monitoring them remains incredibly difficult. While researching maritime surveillance, we discovered a critical loophole:

  • Most tracking systems rely on AIS signals
  • Illegal vessels simply turn off AIS to become invisible

This raised a fundamental question:

“What if we could detect vessels even when they don’t want to be seen?”

That question became the foundation of Ghost Hunter.

🛠️ How We Built It

We designed Ghost Hunter as a multi-stage intelligence pipeline that transforms raw satellite data into actionable insights.

🔹 Phase 1: Physical Detection (Seeing the Invisible)

  • Processed Sentinel-1 SAR satellite imagery
  • Applied physics-based detection (SBCI) to identify ship-like structures
  • Used K-Means clustering to remove ocean noise and isolate vessel candidates
  • Cross-referenced detections with AIS data:
    • AIS present → Normal vessel
    • AIS absent → 🚨 Dark vessel candidate

🔹 Phase 2: Behavioral Intelligence (Understanding Intent)

Detection alone isn’t enough—we needed to understand behavior.

  • Built a CNN classifier to validate real vessels vs noise
  • Designed rule-based behavioral analysis:
    • Vessel proximity (possible coordination)
    • Fleet formations (illegal group activity)
    • Movement patterns (suspicious intent)

We then combined all signals into a risk score (0–100):

$$ \text{Risk Score} = \sum (w_i \cdot f_i) $$

Where:

  • fᵢ = behavioral or detection features
  • wᵢ = importance weights

🔹 Final Output: Intelligence Reports

The system generates:

  • Structured JSON outputs
  • Human-readable reports
  • Clear risk categorization: Low → Critical

This bridges the gap between raw satellite data and real-world enforcement.

📚 What We Learned

This project pushed us across multiple domains:

  • 🌐 Remote Sensing – Understanding SAR imagery and its challenges
  • 🤖 Machine Learning – CNNs, clustering, and feature engineering
  • 📊 Data Fusion – Combining physical + behavioral signals
  • 🧠 Explainable AI – Making outputs interpretable for decision-makers
  • ⚙️ System Design – Building scalable, modular pipelines

One key realization:

Detection is easy. Interpretation is what creates impact.

⚔️ Challenges We Faced

1. 🚧 Lack of Real-Time Data

  • SAR data is large and slow to download
  • Limited free-tier APIs caused delays

2. 🧠 Behavior Modeling Limitations

  • Initially tried LSTMs
  • Failed due to sparse temporal data
  • Switched to spatial + rule-based intelligence

3. ⚖️ Data Imbalance in CNN

  • Mostly ship images → poor generalization
  • Solved by generating synthetic sea patches

4. 💻 Compute Constraints

  • Heavy processing not feasible in real-time
  • Built a lightweight demo pipeline while keeping full system offline-capable

🌱 What’s Next

We see Ghost Hunter evolving into a real-world enforcement tool:

  • 📍 Vessel trajectory tracking using Kalman Filters
  • 🧠 Advanced behavior modeling with temporal data
  • ⚡ Real-time detection with optimized pipelines
  • 📱 Mobile apps for field officers

🌍 Final Thoughts

Ghost Hunter is more than a technical project—it’s a step toward protecting our oceans.

By combining satellite intelligence with AI, we turn invisibility into accountability.

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