Problem Statement Title: Image Analytics for Forest Land Diversion Tree Enumeration

Description: The challenge involves developing an image analytics solution to accurately enumerate trees in forest areas that are earmarked for diversion due to various development projects. This solution aims to provide an efficient and accurate method for counting and assessing the impact of forest land diversion on tree populations.

Domain: Environmental Conservation, Image Analytics, Forestry

Solution Proposal:

Resources Needed:

  • Data Scientists
  • Remote Sensing Experts
  • Image Processing Engineers
  • GIS (Geographic Information System) Specialists

Timeframe:

  • Research and Data Collection: 2-3 months
  • Model Development and Training: 6-8 months
  • Testing and Validation: 2-3 months

Technology/Equipment Needed:

  • High-Resolution Satellite Images
  • Drones (for ground-level image capture)
  • Image Processing Software
  • Machine Learning and Deep Learning Frameworks
  • GIS Software

Team Size:

  • Data Scientists: 3-4 members
  • Remote Sensing Experts: 1-2 members
  • Image Processing Engineers: 2-3 members
  • GIS Specialists: 1-2 members

Scope:

  1. Research and Data Collection:

    • Collect high-resolution satellite images and ground-level images.
    • Gather labeled data for training the image analytics model.

  2. Model Development and Training:

    • Develop a machine learning or deep learning model for tree enumeration.
    • Train the model using labeled images and relevant features.

  3. Testing and Validation:

    • Test the model's accuracy on a diverse set of images.
    • Validate the model's performance against ground-truth data.

  4. Integration with GIS:

    • Integrate the image analytics solution with GIS software.
    • Create a user-friendly interface for data visualization.

Learnings:

  • Proficiency in remote sensing, image processing, and machine learning.
  • Understanding of forest ecology and environmental impact assessment.

Strategy/Plan:

  1. Research and Data Collection: Gather high-resolution images and labeled data.
  2. Model Development and Training: Develop and train the image analytics model.
  3. Testing and Validation: Test the model's accuracy and validate against ground-truth data.
  4. Integration with GIS: Integrate the solution with GIS software for visualization.