16788 Registered Allowed team size: 1 - 6
16788 Registered Allowed team size: 1 - 6

This campaign is over.

Prototype Phase
starts on:
Jan 08, 2023, 03:00 PM ()
ends on:
Jan 21, 2023, 09:00 PM ()


Please go through the SUBMISSION GUIDELINE tab for both the themes.

If you selected the "Theme 1" or "Both" option during registration, your Team Leader will get an invite for the ML platform to submit your solution. Please check your email.


For Theme #1:

  • You may submit your project as many times as you like. Only the final submission will be judged.
  • Make your submission on the ML platform in .csv format.
  • Your model will be evaluated along with your final submission. SDAIA reserves the right to reject any submission if the model is found to be sub par..
  • All projects must contain the following in their submission as a simple writeup of maximum 800 words:
    • Project overview (3-4 sentences explaining what you built)
    • Describe what challenges you faced at preparing the data and how you approached the problem.
    • Describe how you would make your solution more scalable.
    • Declare what open source software you used to build your solution.
    • Describe what you would want to try if you had more time / resources / data to resolve the problem.


For Theme #2:


  • All projects must contain the following in their submission as a simple writeup of maximum 1000 words:
  1. Describe how you build a system that is able to effectively localize the potholes?
  2. Describe how you are able to estimate the magnitude (in depth and area) of the potholes detected?
  3. Tell us if you managed to represent the pothole characteristics via some sort of 3d reconstruction from the visual data alone?
  4. Describe how your proposal shows potential in:
    1. Accurately detecting potholes
    2. Accurately estimating the magnitude of the potholes
    3. Accuracy establishing the most urgent or problematic potholes in the analyzed data.
    4. Potential maturity in the solution as an alternative to Mobile Lidar on pothole detection via Computer Vision.
    5. Level of solution autonomy. How much human intervention does the analysis need? Does it potentially scale well?

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