Abstract: Presented herein are systems and methods for identifying and disambiguating individual objects of interest from a data set. In one or more examples, image data can be received and processed to identify candidate images that may contain one or more objects of interest. The candidate image data can then be processed to segment potential objects of interest from the candidate images. The segmented potential objects of interest can then be processed via one or more analytics to determine whether each potential objects of interest is an object of interest, to determine an object type, and/or to disambiguate specific objects of interest.

Abstract: Presented herein are systems and methods for generating synthetic training data for machine learning models. Images of a particular object (such as an aircraft) can be received and processed to cutout the object (i.e., separate the object from the background) from the received image. The systems and methods described herein can detect areas in the background images to place an object. Once a suitable area has been detected, the cutout object image can be superimposed on the background image at the location determined to be suitable for placing the object. Superimposing the object onto the background image can include blending the two images using a plurality of blending techniques to reduce artifacts that may bias a supervised training process.