Methods and systems for using trained generative adversarial networks to impute 3d data for modeling peril

A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation, comprising:

• one or more processors, and
• one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to:
• receive a semantically-segmented 3D point cloud including elevation information of one or more objects represented within the 3D point cloud;
• generate a gap-filled point cloud based on the point cloud using a generative adversarial network;
• and
• generate a digital map based on the gap-filled point cloud.

A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to:

• receive a semantically-segmented 3D point cloud including elevation information of one or more objects represented within the 3D point cloud;
• generate a gap-filled point cloud based on the point cloud using a generative adversarial network;
• and
• generate a digital map based on the gap-filled point cloud.