Deep machine learning to predict and prevent adverse conditions at structural assets
First Claim
1. A computing system to predict and prevent instances of vegetative overgrowth on utility infrastructure, the computing system comprising:
- at least one processor; and
at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the computing system to;
obtain data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first utility asset, the plurality of images comprising at least a first image and a second image, the first image and the second image being different from each other in at least one of the following attributes;
time of capture, location of capture, angle of capture, data type, and resolution;
input data descriptive of at least the first image and the second image into a machine-learned vegetation overgrowth prediction model that comprises a machine-learned object detection model and a machine-learned vegetation overgrowth neural network;
receive, as an output of the machine-learned object detection model, one or more object feature vectors that respectively describe one or more attributes of one or more objects included in the geographic area;
input the one or more object feature vectors and at least one of the plurality of images into the machine-learned vegetation overgrowth neural network; and
receive, as an output of the machine-learned vegetation overgrowth neural network, at least one prediction regarding the occurrence of a vegetative overgrowth condition at the first utility asset during one or more future time periods, the at least one prediction produced by the machine-learned vegetation overgrowth neural network based at least in part on the one or more object feature vectors and the at least one of the plurality of images.
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Abstract
The present disclosure provides systems and methods that use machine-learned models, such as deep neural networks, to predict and prevent adverse conditions at structural assets. One example method includes obtaining data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first structural asset. The plurality of images include at least a first image captured at a first time and a second image captured at a second time that is different than the first time. The method includes inputting data descriptive of at least the first image, the first time, the second image, and the second time into a condition prediction model. The method includes receiving, as an output of the condition prediction model, at least one prediction regarding the occurrence of an adverse condition at the first structural asset during one or more future time periods.
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Citations
19 Claims
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1. A computing system to predict and prevent instances of vegetative overgrowth on utility infrastructure, the computing system comprising:
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at least one processor; and at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the computing system to; obtain data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first utility asset, the plurality of images comprising at least a first image and a second image, the first image and the second image being different from each other in at least one of the following attributes;
time of capture, location of capture, angle of capture, data type, and resolution;input data descriptive of at least the first image and the second image into a machine-learned vegetation overgrowth prediction model that comprises a machine-learned object detection model and a machine-learned vegetation overgrowth neural network; receive, as an output of the machine-learned object detection model, one or more object feature vectors that respectively describe one or more attributes of one or more objects included in the geographic area; input the one or more object feature vectors and at least one of the plurality of images into the machine-learned vegetation overgrowth neural network; and receive, as an output of the machine-learned vegetation overgrowth neural network, at least one prediction regarding the occurrence of a vegetative overgrowth condition at the first utility asset during one or more future time periods, the at least one prediction produced by the machine-learned vegetation overgrowth neural network based at least in part on the one or more object feature vectors and the at least one of the plurality of images. - View Dependent Claims (2, 3, 4)
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5. A computer-implemented method to predict and prevent adverse conditions at structural assets, the method comprising:
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obtaining, by one or more computing devices, data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first structural asset, wherein the plurality of images respectively depict the geographic area at a plurality of different times, the plurality of images comprising at least a first image captured at a first time and a second image captured at a second time that is different than the first time; inputting, by the one or more computing devices, data descriptive of at least the first image, the first time, the second image, and the second time into a condition prediction model, the condition prediction model comprising a machine-learned object detection model and a machine-learned condition prediction neural network; receiving, as an output of the machine-learned object detection model, one or more object feature vectors that respectively describe one or more attributes of one or more objects included in the geographic area; inputting the one or more object feature vectors and at least one of the plurality of images into the machine-learned condition prediction neural network; and receiving, by the one or more computing devices as an output of the machine-learned condition prediction neural network, at least one prediction regarding the occurrence of an adverse condition at the first structural asset during one or more future time periods, the at least one prediction produced by the machine-learned condition prediction neural network based at least in part on the one or more object feature vectors and the at least one of the plurality of images. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system to predict and prevent adverse conditions at structural assets, the system comprising:
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an image database that stores a plurality of images of geographic areas, wherein the plurality of images comprise at least a first ground-level image that depicts at least a portion of a first geographic area that contains a first structural asset, and wherein the plurality of images further comprise at least a first overhead image that depicts at least a portion of the first geographic area that contains the first structural asset; and a machine learning computing system, wherein the machine learning computing system comprises at least one processor and at least one memory that stores a condition prediction model usable to predict the occurrence of adverse conditions at structural assets during one or more future time periods based on input imagery, wherein the condition prediction model comprises a machine-learned object detection model and a machine-learned condition prediction neural network, and wherein the machine learning computing system is operable to; obtain the first ground-level image of the first geographic area; obtain the first overhead image of the first geographic area; input data descriptive of at least the first ground-level image into the machine-learned objection detection model; receive, as an output of the machine-learned object detection model, one or more object feature vectors that respectively describe one or more attributes of one or more objects included in the first geographic area; input the one or more object feature vectors and the first overhead image into the machine-learned condition prediction neural network; and receive at least one prediction regarding the occurrence of an adverse condition at the first structural asset as an output of the machine-learned condition prediction neural network, the at least one prediction produced by the machine-learned condition prediction neural network based at least in part on the one or more object feature vectors and the first overhead image. - View Dependent Claims (17, 18, 19)
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Specification