In various examples, health of a high definition (HD) map may be monitored to determine whether inaccuracies exist in one or more layers of the HD map. For example, as one or more vehicles rely on the HD map to traverse portions of an environment, disagreements between perception of the one or more vehicles, map layers of the HD map, and/or other disagreement types may be identified and aggregated. Where errors are identified that indicate a drop in health of the HD map, updated data may be crowdsourced from one or more vehicles corresponding to a location of disagreement within the HD map, and the updated data may be used to update, verify, and validate the HD map.