MAST: Multi-Agent System for Map Quality Assurance

A hybrid multi-agent pipeline combining rule-based checks, ML anomaly detection, and LLM reasoning to validate and explain geospatial data inconsistencies at scale.

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🚀 Inspiration

Geospatial data is critical for navigation, logistics, and urban planning. However, ensuring its quality at scale remains a major challenge due to inconsistencies, missing attributes, and structural errors.

We were inspired to build a system that goes beyond simple validation — one that can intelligently analyze, detect, and explain issues in map data using a combination of rule-based logic, machine learning, and contextual reasoning.

💡 What it does

MAST (Multi-Agent System for Map Quality Assurance) is an AI-powered system designed to automatically validate and analyze geospatial data.

It detects issues such as:

  • Geometry inconsistencies
  • Missing or incorrect attributes
  • Structural anomalies
  • Contextual mismatches

The system classifies issues into PASS, WARNING, or FAIL, and provides detailed explanations for each decision.

🧠 How we built it

The system follows a multi-stage pipeline:

  1. Geometry Validation Layer
    Rule-based checks to validate spatial structures and constraints.

  2. Feature Extraction Layer
    Extracts meaningful attributes and metadata from map data.

  3. ML Anomaly Detection
    Identifies unusual patterns using machine learning techniques.

  4. Contextual Reasoning Engine
    Evaluates features in context to determine real-world correctness.

  5. Decision Fusion Module
    Combines outputs from all components into a final verdict.

  6. Explanation Layer
    Generates human-readable explanations for each decision.

⚙️ Challenges we ran into

  • Balancing deterministic rules with probabilistic ML outputs
  • Designing a scalable multi-agent architecture
  • Ensuring meaningful and interpretable explanations
  • Handling noisy and incomplete geospatial data

🏆 Accomplishments that we're proud of

  • Built a complete end-to-end validation pipeline
  • Designed a modular multi-agent architecture
  • Achieved consistent and explainable results
  • Successfully scaled the system for multiple inputs

📚 What we learned

  • Importance of combining rule-based and ML approaches
  • Designing robust multi-agent systems
  • Trade-offs between accuracy and interpretability
  • Real-world challenges in geospatial data validation

🔮 What's next for MAST

  • Integration with real-time map APIs
  • Advanced LLM-based explanation generation
  • Scalable deployment using cloud infrastructure
  • Enhanced visualization dashboards for insights

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