1. A system for broad area geospatial object detection using synthetically-generated training images for improved training of a deep learning model comprising a computing device comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions: (a) retrieves a digital model of an object of interest from a data store; (b) creates a flattened image from the retrieved digital model; (c) compares the flattened image to a real geospatial image comprising an instance of the object of interest and associated background; (d) scales the flattened image to align with the real geospatial image of the instance of the object of interest and separates the flattened image from the background of the real image; (e) applies a plurality of environmental effects to replicate seasonal, time of day, associated brightness, and environmental factors consistent with a geographic location of the real background image to create a plurality of modified synthetic images; (f) creates a plurality of shadowed, modified flattened images for the object of interest as if it were physically located and oriented where it would be affected by real-time and real-world shadowing; (g) adjusts the shadowed, modified flattened images to resemble the real image; (h) identifies and demarcates a footprint associated with each of the shadowed, modified flattened images; (i) overlays the demarcated footprint onto a real image; (j) generates a labeled corpus of manipulated synthetic training data comprising at least a portion of the modified flattened images; and (k) trains a deep learning model comprising a convolutional neural network to recognize objects of the same type as the object of interest in geospatial images.