Patent Document ID: 9767565
Application ID: 15194541
Patent Status: 1

Claim One:
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 3-dimensional model of an object of interest from an established data store; (b) creates a flattened, 2-dimensional modeled image from the 3-dimensional model; (c) compares the flattened modeled image to a real geospatial image comprising an instance of the object of interest and associated background; (d) scales the flattened modeled image to align with the real geospatial image of the instance of the object of interest and upon successful alignment, separates the flattened modeled image from the background of the real image in order to fine tune components of the flattened modeled image, which include smoothing edges or color matching to simulate 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 2-dimensional synthetic images for the 3-dimensional object 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 synthetic 2-dimensional images by pixelating and blurring or focusing to resemble the real image; (h) identifies and demarcates a footprint associated with each of the shadowed, modified synthetic 2-dimensional images; (i) overlays the demarcated footprint onto a real image and masks the background colors surrounding the synthetic image to become transparent such that overlay onto the real image does not obscure existing images to create a manipulated synthetic image; (j) generates a labeled corpus of manipulated synthetic training data comprising a plurality of modified 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.