Abstract: Systems and methods to generate synthetic images for use in a machine learning training set. The process begins with accessing a database of real-world 3-D images of equipment in a power grid, the 3-D images of equipment include 3-D measurements to create a dimensionally accurate and photorealistic model of the equipment. Optionally, the 3-D images could be aged or weathered using imaging editing software. Next, a database of real-world photographs of scenes in which the equipment is installed is accessed. Optionally, the identical scenes can be captured at different times of day, different times of the year, and at different perspectives. Next, using image editing software, the 3-D images of equipment is inserted into at least one of the scenes to form a synthetic image based on a combination of the equipment and the scene in which each of the equipment and the scene were previously captured independently of each other.