This paper describes a system that would suggest parking choices to a user based on a destination selected and information previously extracted from regularly gathered aerial and/or street-level imagery. The system provides information on the expected occupancy of the suggested parking options at the estimated time of user arrival. Further, this system would be able to provide information on which parking options are free and which ones are commercial and the pricing information, also sensitive to the time of the user arrival. The occupancy is predicted via the application of machine learning models to the regularly gathered imagery. The imagery can be analyzed with machine learning models specialized to look for ground level parking lots with or without vehicles in them to find the locations of any open-air parking or multi-level parking lots with the roof level available for parking.
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Mayster, Yan, "Real-Time Parking Lot Guidance", Technical Disclosure Commons, (June 11, 2020)