Systems and methods for generating synthetic satellite image training data for machine learning models

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.