Systems and methods for automated detection of changes in extent of structures using imagery
Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align an outline of a structure at a first instance of time to pixels within an image depicting the structure, the image captured at a second instance of time; assess a degree of alignment between the outline and the pixels within the image depicting the structure, using a machine learning model to generate an alignment confidence score; determine an existence of a change in extent of the structure based upon the alignment confidence score indicating that the outline and the pixels within the image are not aligned; identify a shape of the change in extent of the structure; and store the shape of the change in extent of the structure.
1 . A non-transitory computer readable medium storing computer-executable instructions that when executed by one or more computer processors cause the one or more computer processors to:
align an outline of a structure at a first instance of time to pixels within an image depicting the structure by overlaying the outline of the structure onto the pixels within the image depicting the structure, the image captured at a second instance of time;
assess a degree of alignment between the outline and the pixels within the image depicting the structure, using a machine learning model to generate an alignment confidence score;
determine an existence of a change in extent of the structure based upon the alignment confidence score indicating that the outline and the pixels within the image depicting the structure are not aligned;
identify a shape of the change in extent of the structure; and
store the shape of the change in extent of the structure.
2 . The non-transitory computer readable medium of claim 1 , wherein the machine learning model is a convoluted neural network image classifier model.
3 . The non-transitory computer readable medium of claim 1 , wherein the machine learning model is a generative adversarial network image classifier model.
4 . The non-transitory computer readable medium of claim 1 , further storing computer-executable instructions that when executed by one or more computer processors cause the one or more computer processors to:
identify the shape of the change in the structure using any one or more of: a point cloud estimate, a convolutional neural network, a generative adversarial network, and a feature detection technique.
5 . The non-transitory computer readable medium of claim 1 , wherein aligning the outline further comprises:
creating the outline using an image of the structure captured at the first instance of time.
6 . The non-transitory computer readable medium of claim 1 , wherein the outline is a first outline and wherein the alignment confidence score is determined by analyzing shape intersection between the first outline and a second outline of the structure depicted within the pixels of the image.
7 . The non-transitory computer readable medium of claim 1 , wherein the outline is a vector boundary describing an extent of the structure at the first instance of time.
8 . The non-transitory computer readable medium of claim 1 , wherein the first instance of time is before the second instance of time.
9 . The non-transitory computer readable medium of claim 1 , wherein the first instance of time is after the second instance of time.
10 . The non-transitory computer readable medium of claim 1 , wherein aligning the outline further comprises:
detecting edges of the structure in the image;
determining one or more shift distance between the outline and one or more edges of the detected edges of the structure in the image; and
shifting the outline by the shift distance.
11 . The non-transitory computer readable medium of claim 10 , further storing computer-executable instructions that when executed by one or more computer processors cause the one or more computer processors to:
determine a structural modification based on the existence of the change and on a comparison between the outline and the pixels within the image depicting the structure, after the outline is shifted by the shift distance.
12 . A method, comprising:
aligning, with one or more computer processors, an outline of a structure at a first instance of time to pixels within an image depicting the structure by overlaying the outline of the structure onto the pixels within the image depicting the structure, the image captured at a second instance of time;
assessing, with the one or more computer processors, a degree of alignment between the outline and the pixels within the image depicting the structure, using a machine learning model to generate an alignment confidence score; and
determining, with the one or more computer processors, an existence of a change in the structure based upon the alignment confidence score indicating that the outline and the pixels within the image depicting the structure are not aligned;
identifying, with the one or more computer processors, a shape of the change in extent of the structure; and
storing the shape of the change in extent of the structure.
13 . The method of claim 12 , wherein the machine learning model is at least one of a convoluted neural network image classifier model and a generative adversarial network image classifier model.
14 . The method of claim 12 , wherein identifying, with the one or more computer processors, the shape of the change in extent of the structure utilizes any one or more of: a point cloud estimate, a convolutional neural network, a generative adversarial network, and a feature detection technique.
15 . The method of claim 12 , wherein aligning the outline further comprises:
creating, with the one or more computer processors, the outline using an image of the structure captured at the first instance of time.
16 . The method of claim 12 , wherein the outline is a first outline, and wherein the alignment confidence score is determined by analyzing shape intersection between the first outline and a second outline of the structure depicted within the pixels of the image.
17 . The method of claim 12 , wherein the outline is a vector boundary describing an extend of the structure at the first instance of time.
18 . The method of claim 12 , wherein the first instance of time is before the second instance of time or the first instance of time is after the second instance of time.
19 . The method of claim 12 , wherein aligning the outline further comprises:
detecting edges of the structure in the image;
determining one or more shift distance between the outline and one or more edges of the detected edges of the structure in the image; and
shifting the outline by the shift distance.
20 . The method of claim 19 , further comprising:
determining, with the one or more computer processors, a structural modification based on the existence of the change and on a comparison between the outline and the pixels within the image depicting the structure, after the outline is shifted by the shift distance.