Problem Statement Title: Artificial Intelligence-Driven Digitization of Cadastral Maps

Description: Develop an AI-driven solution to digitize and automate the process of converting paper-based cadastral maps into digital format, improving accuracy and efficiency in land record management.

Domain: Geospatial, Land Records, AI/ML, Data Digitization

Solution Proposal:

Resources Needed:

  • GIS Experts
  • Data Scientists
  • AI/ML Specialists
  • Software Developers
  • Data Entry Operators (for manual validation)

Timeframe:

  • Requirement Analysis: 2-3 months
  • Model Development: 6-8 months
  • Testing and Validation: 3-4 months
  • Deployment and Integration: 4-6 months

Scope:

  1. Requirement Analysis:

    • Understand the structure and details of cadastral maps.
    • Identify challenges in digitization and conversion.

  2. Data Collection and Preparation:

    • Collect high-resolution images of cadastral maps.
    • Convert images into a standardized format for AI processing.

  3. AI Model Development:

    • Develop AI/ML models for detecting land parcel boundaries, text extraction, and feature recognition.
    • Implement deep learning techniques for accurate detection.

  4. Automation of Conversion:

    • Design a pipeline to process cadastral map images through AI models.
    • Extract parcel boundaries, landmarks, and textual information.

  5. Validation and Manual Review:

    • Include a manual validation step to ensure accuracy.
    • Correct inaccuracies detected by the AI models.

  6. Data Integration:

    • Integrate digitized cadastral data into the existing land records management system.
    • Develop APIs for easy data exchange.

Technology Stack:

  • GIS Software (ArcGIS, QGIS)
  • Deep Learning Frameworks (TensorFlow, PyTorch)
  • Computer Vision Libraries (OpenCV)
  • Optical Character Recognition (OCR) Libraries (Tesseract)
  • Data Visualization Tools (Tableau, Power BI)

Learnings:

  • Gain expertise in geospatial data processing and AI-driven image analysis.
  • Understand the challenges of accurately detecting features from complex cadastral maps.
  • Learn about data integration and interoperability with existing land records systems.

Strategy/Plan:

  1. Requirement Analysis: Understand cadastral map characteristics and challenges.
  2. Data Collection: Gather high-resolution images of cadastral maps.
  3. AI Model Development: Develop AI models for boundary detection, text extraction, and feature recognition.
  4. Conversion Pipeline: Design a pipeline for automated conversion of maps.
  5. Validation and Correction: Include manual validation to correct AI-detected inaccuracies.
  6. Data Integration: Integrate digitized data into land records systems through APIs.