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: Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

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: Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

Abstract: A fingerprint identification system comprising a smart device, a fingerprint scanner, a processor coupled to a transceiver and to the fingerprint scanner, and a digital storage element coupled to the processor. The digital storage element stores logic that causes the processor to: activate the fingerprint scanner to scan a user's finger, identify a feature and multiple minutia of the user's fingerprint, and generate a digital fingerprint string(s) indicative of a position of each minutia relative to the feature. The processor is then caused to combine the digital fingerprint string(s) with a first cryptographic salt to generate a first hash. The first hash is compared to a first hash signature to determine if the first hash represents an authentic fingerprint. If authentic, the processor combines the digital fingerprint string(s) with a second cryptographic salt to generate a second hash. This second hash is transmitted to one or more servers.

Abstract: Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

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, 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: Automated methods and systems are disclosed, including a method comprising: obtaining a first three-dimensional-data point cloud of a horizontal surface of an object of interest, the first three-dimensional-data point cloud having a first resolution and having a three-dimensional location associated with each point in the first three-dimensional-data point cloud; capturing one or more aerial image, at one or more oblique angle, depicting at least a vertical surface of the object of interest; analyzing the one or more aerial image with a computer system to determine three-dimensional locations of additional points on the object of interest; and updating the first three-dimensional-data point cloud with the three-dimensional locations of the additional points on the object of interest to create a second three-dimensional-data point cloud having a second resolution greater than the first resolution of the first three-dimensional-data point cloud.

Abstract: Automated methods and systems are disclosed, including a method comprising: capturing images and three-dimensional LIDAR data of a geographic area with an image capturing device and a LIDAR system, the images depicting an object of interest and the three-dimensional LIDAR data including the object of interest, the image capturing device capturing the images of a vertical surface of the object of interest at one or more oblique angle, and the LIDAR system capturing the three-dimensional LIDAR data of a horizontal surface of the object of interest at a nadir angle; analyzing the images with a computer system to determine three dimensional locations of points on the object of interest; and updating the three-dimensional LIDAR data with the three dimensional locations of points on the object of interest determined by analyzing the images to create a 3D point cloud having a resolution greater than a resolution of the three-dimensional LIDAR data.

Abstract: Automated methods and systems are disclosed, including a method comprising: capturing images and three-dimensional LIDAR data of a geographic area with an image capturing device and a LIDAR system, the images depicting an object of interest and the three-dimensional LIDAR data including the object of interest, the image capturing device capturing the images of a vertical surface of the object of interest at one or more oblique angle, and the LIDAR system capturing the three-dimensional LIDAR data of a horizontal surface of the object of interest at a nadir angle; analyzing the images with a computer system to determine three dimensional locations of points on the object of interest; and updating the three-dimensional LIDAR data with the three dimensional locations of points on the object of interest determined by analyzing the images to create a 3D point cloud having a resolution greater than a resolution of the three-dimensional LIDAR data.

Abstract: Automated methods and systems are disclosed, including a method comprising: capturing images and three-dimensional LIDAR data of a geographic area with an image capturing device and a LIDAR system, as well as location and orientation data for each of the images corresponding to the location and orientation of the image capturing device capturing the images, the images depicting an object of interest and the three-dimensional LIDAR data including the object of interest; storing the three-dimensional LIDAR data on a non-transitory computer readable medium; analyzing the images with a computer system to determine three dimensional locations of points on the object of interest; and updating the three-dimensional LIDAR data with the three dimensional locations of points on the object of interest determined by analyzing the images to create a 3D point cloud.

Abstract: A fingerprint identification system comprising a smart device, a fingerprint scanner, a processor coupled to a transceiver and to the fingerprint scanner, and a digital storage element coupled to the processor. The digital storage element stores logic that causes the processor to: activate the fingerprint scanner to scan a user's finger, identify a feature and multiple minutia of the user's fingerprint, and generate a digital fingerprint string(s) indicative of a position of each minutia relative to the feature. The processor is then caused to combine the digital fingerprint string(s) with a first cryptographic salt to generate a first hash. The first hash is compared to a first hash signature to determine if the first hash represents an authentic fingerprint. If authentic, the processor combines the digital fingerprint string(s) with a second cryptographic salt to generate a second hash. This second hash is transmitted to one or more servers.

Abstract: A fingerprint identification system comprising a smart device, a fingerprint scanner, a processor coupled to a transceiver and to the fingerprint scanner, and a digital storage element coupled to the processor. The digital storage element stores logic that causes the processor to: activate the fingerprint scanner to scan a user's finger, identify a feature and multiple minutia of the user's fingerprint, and generate a digital fingerprint string(s) indicative of a position of each minutia relative to the feature. The processor is then caused to combine the digital fingerprint string(s) with a first cryptographic salt to generate a first hash. The first hash is compared to a first hash signature to determine if the first hash represents an authentic fingerprint. If authentic, the processor combines the digital fingerprint string(s) with a second cryptographic salt to generate a second hash. This second hash is transmitted to one or more servers.

Abstract: A fingerprint identification system comprising a smart device, a fingerprint scanner, a processor coupled to a transceiver and to the fingerprint scanner, and a digital storage element coupled to the processor. The digital storage element stores logic that causes the processor to: activate the fingerprint scanner to scan a user's finger, identify a feature and multiple minutia of the user's fingerprint, and generate a digital fingerprint string(s) indicative of a position of each minutia relative to the feature. The processor is then caused to combine the digital fingerprint string(s) with a first cryptographic salt to generate a first hash. The first hash is compared to a first hash signature to determine if the first hash represents an authentic fingerprint. If authentic, the processor combines the digital fingerprint string(s) with a second cryptographic salt to generate a second hash. This second hash is transmitted to one or more servers.

Abstract: Automated methods and systems are disclosed, including a method comprising: capturing images and three-dimensional LIDAR data of a geographic area with an image capturing device and a LIDAR system, as well as location and orientation data for each of the images corresponding to the location and orientation of the image capturing device capturing the images, the images depicting an object of interest and the three-dimensional LIDAR data including the object of interest; storing the three-dimensional LIDAR data on a non-transitory computer readable medium; analyzing the images with a computer system to determine three dimensional locations of points on the object of interest; and updating the three-dimensional LIDAR data with the three dimensional locations of points on the object of interest determined by analyzing the images to create a 3D point cloud.

Abstract: Automated methods and systems of creating three dimensional LIDAR data are disclosed, including a method comprising capturing images of a geographic area with one or more image capturing devices as well as location and orientation data for each of the images corresponding to the location and orientation of the one or more image capturing devices capturing the images, the images depicting an object of interest; capturing three-dimensional LIDAR data of the geographic area with one or more LIDAR system such that the three-dimensional LIDAR data includes the object of interest; storing the three-dimensional LIDAR data on a non-transitory computer readable medium; analyzing the images with a computer system to determine three dimensional locations of points on the object of interest; and updating the three-dimensional LIDAR data with the three dimensional locations of points on the object of interest determined by analyzing the images.