Structure change detection using image segmentation
AssigneeUnited Services Automobile Association (USAA)
InventorRodrigo Escandon
Life of this patent
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Abstract
A system for detecting structures may include one or more processors that receive an aerial image associated with a property of interest, identify boundaries associated with the property of interest, apply a structure detection model to the aerial image to detect contours of one or more structures within the boundaries, and calculate a surface area of the detected one or more structures. In an embodiment, the structure detection model uses machine learning and past image data to identify one or more structures associated with a property of interest. The structure detection system may identify changes in an identified structure over time based on a calculated surface area in one or more subsequent aerial images.
Claims20
A system for detecting structures in an aerial image, comprising:
- one or more processors configured to:
- receive an aerial image associated with a property of interest;
- identify one or more boundaries associated with the property of interest;
- apply a structure detection model to the aerial image to detect contours of one or more structures within the boundaries of the property of interest;
- calculate a surface area of the detected one or more structures,
- determine the surface area is less than a threshold amount different than a previous surface area;
- and
- based on determining the surface area is less than a threshold amount different than the previous surface area;
- identify a shadow pattern corresponding to the property of interest;
- compare the shadow pattern to an expected shadow pattern;
- determine the shadow pattern does not match the expected shadow pattern;
- and
- based on determining the shadow pattern does not match the expected shadow pattern, adjust the surface area.
A method for detecting structural changes within a property of interest, comprising:
- receiving a first set of aerial image data collected at a first timepoint, wherein the first set of aerial image data comprises the property of interest, and wherein the property of interest comprises one or more structures;
- determining a type of structure of the one or more structures;
- determining one or more boundaries of the property of interest;
- applying a structure detection model to the first set of aerial image data based on the determined structure type, wherein applying the structure detection model comprises:
- masking other structures outside the one or more boundaries of the property of interest;
- isolating the one or more structures within the one or more boundaries of the property of interest;
- and
- identifying a first set of contours of the one or more structures;
- calculating a first surface area of the first set of contours of the one or more structures;
- determining the first surface area is less than a threshold amount different than a previous first surface area;
- and
- based on determining the first surface area is less than the threshold amount different than the previous first surface area;
- identifying a shadow pattern corresponding to the property of interest;
- comparing the shadow pattern to an expected shadow pattern;
- determining the shadow pattern does not match the expected shadow pattern;
- and
- based on determining the shadow pattern does not match the expected shadow pattern, adjusting the first surface area.
A tangible, non-transitory, computer-readable medium, comprising computer-readable instructions that, when executed by one or more processors, cause the one or more processors to:
- receive aerial image data associated with a property of interest;
- identify one or more boundaries of the property of interest;
- apply, using machine learning, a structure detection model to the aerial image data, to identify one or more structures within the one or more boundaries of the property of interest and contours of the one or more structures, wherein the machine learning is configured to utilize past image data to detect patterns that identify properties associated with structures;
- calculate a surface area of one or more contours associated with the one or more structures;
- determine the calculated surface area is less than a threshold amount different than a previous surface area;
- and
- based on determining the calculated surface area is less than a threshold amount different than a previous surface area;
- identify a shadow pattern corresponding to the property of interest;
- compare the shadow pattern to an expected shadow pattern;
- determine the shadow pattern does not match the expected shadow pattern;
- and
- based on determining the shadow pattern does not match the expected shadow pattern, adjust the calculated surface area;
- and
- use the adjusted calculated surface area to detect changes in the surface area over time.
Dependencies are inferred from the claim text.