Abstract: 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.

Abstract: 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, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

Abstract: Systems and methods are disclosed for using spatial filter to reduce bundle adjustment block size, including a method comprising: assigning a plurality of feature tracks to a voxel corresponding to a region of a geographic area, the voxel having a length, a width and a height, each feature track including a geographic coordinate within the region, a first image identifier identifying a first image, a second image identifier identifying a second image, a first pixel coordinate identifying a first location of a first feature in the first image, and a second pixel coordinate identifying a second location of the first feature within the second image; determining a quality metric value of the feature tracks assigned to the voxel; and conducting bundle adjustment on a subset of the feature tracks assigned to the voxel, the subset of the feature tracks based on the quality metric value.

Abstract: 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, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

Abstract: 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, with an image classifier model, a structure shape of a structure at a first instance of time to pixels within an aerial image depicting the structure captured at a second instance of time; assess a degree of alignment between the structure shape and the pixels, so as to classify similarities between the structure depicted within the pixels and the structure shape using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the structure shape and the pixels within the aerial image are aligned.

Abstract: 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, with an image classifier model, a structure shape of a structure at a first instance of time to pixels within an aerial image depicting the structure captured at a second instance of time; assess a degree of alignment between the structure shape and the pixels, so as to classify similarities between the structure depicted within the pixels and the structure shape using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the structure shape and the pixels within the aerial image are aligned.

Abstract: Systems and methods are disclosed for using spatial filter to reduce bundle adjustment block size, including a method comprising: assigning a plurality of feature tracks to a voxel corresponding to a region of a geographic area, the voxel having a length, a width and a height, each feature track including a geographic coordinate within the region, a first image identifier identifying a first image, a second image identifier identifying a second image, a first pixel coordinate identifying a first location of a first feature in the first image, and a second pixel coordinate identifying a second location of the first feature within the second image; determining a quality metric value of the feature tracks assigned to the voxel; and conducting bundle adjustment on a subset of the feature tracks assigned to the voxel, the subset of the feature tracks based on the quality metric value.