Controlled image manipulations using semantic labels
AssigneeMayachitra, Inc.
InventorsChandrakanth Gudavalli, Erik Rosten, Lakshmanan Nataraj, Shivkumar Chandrasekaran, Bangalore S. Manjunath
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Abstract
Systems and methods herein describe an image generation system that accesses a semantic map and satellite image, manipulates the semantic map image, trains a machine learning framework using a set of map and satellite image pairs, uses the trained machine learning framework to generate a manipulated satellite image based on the manipulated semantic map, generates a blended satellite image based on a combination of the manipulated satellite image data and the originally accessed satellite image data, and stores the blended satellite image.
Claims20
A method comprising:
- accessing map image data that comprises a plurality of pixels associated with a plurality of map item labels;
- accessing satellite image data associated with the map image data;
- manipulating at least a portion of map item labels of the plurality of map item labels to generate manipulated map image data;
- training a machine learning framework using a set of map and satellite image pairs, the set of map and satellite image pairs depicting a geographic region visually similar to the accessed map image data and the accessed satellite image data;
- using the trained machine learning framework to generate manipulated satellite image data based on the manipulated map image data;
- generating a blended satellite image based on a combination of the manipulated satellite image data and the accessed satellite image data;
- and
- storing the blended satellite image.
A system comprising:
- one or more hardware processors;
- and
- a memory storing instructions that, when executed by the one or more hardware processors, configure the system to perform operations comprising:
- accessing map image data that comprises a plurality of pixels associated with a plurality of map item labels;
- accessing satellite image data associated with the map image data;
- manipulating at least a portion of map item labels of the plurality of map item labels to generate manipulated map image data;
- training a machine learning framework using a set of map and satellite image pairs, the set of map and satellite image pairs depicting a geographic region visually similar to the accessed map image data and the accessed satellite image data;
- using the trained machine learning framework to generate manipulated satellite image data based on the manipulated map image data;
- generating a blended satellite image based on a combination of the manipulated satellite image data and the accessed satellite image data;
- and
- storing the blended satellite image.
A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising:
- accessing map image data that comprises a plurality of pixels associated with a plurality of map item labels;
- accessing satellite image data associated with the map image data;
- manipulating at least a portion of map item labels of the plurality of map item labels to generate manipulated map image data;
- training a machine learning framework using a set of map and satellite image pairs, the set of map and satellite image pairs depicting a geographic region visually similar to the accessed map image data and the accessed satellite image data;
- using the trained machine learning framework to generate manipulated satellite image data based on the manipulated map image data;
- generating a blended satellite image based on a combination of the manipulated satellite image data and the accessed satellite image data;
- and
- storing the blended satellite image.
Dependencies are inferred from the claim text.