Labeling training data using high-volume navigation data

A computer-readable medium comprising a set of instructions that when executed, cause a processor to:

• retrieve a set of navigation data and image data from a set of egos navigating within an environment comprising at least one feature;
• generate a three-dimensional (3D) model of the environment using the navigation data and image data of at least a subset of the set of egos, the 3D model comprising a virtual representation of the at least one feature of the environment;
• identify a machine learning label associated with the at least one feature within the image data;
• receive second navigation data and second image data from a second ego not included within the set of egos, the second ego navigating within the environment, the second image data including the at least one feature;
• and
• automatically generate a machine learning label for the at least one feature depicted within the second image data.

A system comprising:

• a set of egos;
• and
• a processor in communication with the set of egos, the processor configured to:
• retrieve a set of navigation data and image data from a set of egos navigating within an environment comprising at least one feature;
• generate a three-dimensional (3D) model of the environment using the navigation data and image data of at least a subset of the set of egos, the 3D model comprising a virtual representation of the at least one feature of the environment;
• identify a machine learning label associated with the at least one feature within the image data;
• receive second navigation data and second image data from a second ego not included within the set of egos, the second ego navigating within the environment, the second image data including the at least one feature;
• and
• automatically generate a machine learning label for the at least one feature depicted within the second image data.