Automated Landmark Visibility Calculation for Navigation Data

The patent replaces manual mechanical processes (human operators going through street level imagery) with automated computer vision algorithms and deep learning models. The system uses neural networks to automatically identify landmarks in street level images and calculate visibility distances, eliminating the need for manual intervention while maintaining accuracy and significantly reducing time requirements.

The patent creates automated digital copies of the manual process by training deep learning models on labeled datasets. The system learns from examples of manually identified landmarks and replicates this capability automatically, allowing it to process vast amounts of imagery without human intervention while preserving the accuracy characteristics of manual methods.

The patent enables continuous automated updating of navigation data by processing new street level imagery as it becomes available. The system continuously identifies new landmarks and updates visibility distances without interruption, ensuring the navigation database remains current with changing road geometries and newly constructed landmarks while maintaining quality through consistent algorithmic processing.

The system performs self-updating by automatically detecting changes in the environment through image analysis. When new imagery is processed, the system independently identifies new landmarks, calculates their visibility distances, and updates the navigation database without requiring human intervention or external triggers, enabling continuous adaptation to environmental changes.