Direct personnel requirements in the field are reduced from 4 to 1, while surveying rate is more than doubled. This has knock-on effects on the number of support staff required, the size of the required aircraft and the amount of fuel consumed.

Using machine vision as the primary detection mechanism reduces the component of variation in the counts that is due to experience or fatigue-level variations amongst crew members.

The imagery that is collected represents a rich data source that may be repurposed afterward for other scientific studies on spatio-temporal variation in vegetation, species distribution (for any species that can be identified in the photos), water levels, game paths etc.

As more training data is gathered and machine vision algorithms improve dramatically from one year to the next, the system continues to improve. Even surveys that were conducted in the past may be reprocessed when our detection abilities improve, providing improved accuracy after-the-fact. Alternatively, newer surveys can be processed with older neural networks to provide counts that are directly comparable to older data in order to spot trends more cleanly.