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This paper proposes a novel approach to processing and utilizing aerial imagery and data for mapping. With the advancement of unmanned aerial systems (UAS), airborne data has gained considerable momentum in recent decades, revolutionizing methodologies of geographic research, law enforcement, agriculture, and mapping. Specifically, advances in consumer electronics significantly eased access to UAS and development in parallel computing has enabled machine learning on large data sets. This paper proposes an alternative method to the current practice of processing airborne data sets. Instead of obtaining aerial imagery of an object and merely presenting those objects as pixels on a map, the proposed method uses machine learning techniques to recognize the object, assign parameters to the object and render the object in a fashion that is most efficient and understandable to the end user. The object is no longer merely a set of pixels but a list of classified objects with associated parameters, an abstraction analogous to cognitive processes. This approach allows a single data set to be used in multiple fashions. For example, the data can be used to simulate changes in the environment or render the environment in different scenarios. This paper explores the methodology for collecting airborne data sets for this process and presents several cases of this using flight test data.

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