Abstract: A system may include a memory and a processor cooperating therewith to obtain geospatial image data from a plurality of different types of sensors and generate a three-dimensional (3D) geospatial model therefrom. The processor may further determine a reference image within the 3D geospatial model based upon synthetically positioning an image sensor within the 3D geospatial model, and perform change detection between a collected image and the reference image based upon semantic change detection using deep learning.

Abstract: Systems (100) and methods (300, 400) for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images (1011, 1050, 1231, 1539, 0001, 0102, 0900, 1678, 0500, 0020, 0992, 1033, 1775, 1829) based on at least one first attribute thereof; generating a screen page (1102-1106) comprising an array (1206) defined by a plurality of cells (1208) in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window (802) of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.

Abstract: Systems (100) and methods (300) for efficient video analysis. The methods involve: automatically identifying features of at least one feature class which are contained in a first video stream; simultaneously generating a plurality of first video chips (904) using first video data defining the first video stream; displaying an array comprising the first video chips within a graphical user interface window; and concurrently playing the first video chips. Each of the first video chips comprises a segment of the first video stream which includes at least one identified feature.

Abstract: Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating chip images using image data defining at least a first image and video chips using video data defining at least a first video stream. Thereafter, an array is displayed which comprises grid cells in which at least a portion of the chip images is presented, at least a portion of the video chips is presented, or a portion of the chip images and a portion of the video chips are presented. Each chip image comprises a panned-only view, a zoomed-only view, or a panned-and-zoomed view of the first image including a visual representation of at least one first object of a particular type. Each of the video chips comprises a segment of the first video stream which include a visual representation of at least one second object of the particular type.

Abstract: Systems (100) and methods (300) for efficiently and accurately detecting changes in feature data. The methods generally involve: determining first vectors for first features extracted from a first image using pixel information associated therewith; comparing the first vectors with second vectors defined by spatial feature data; classifying the first features into a plurality of classes based on the results of the vector comparisons; and analyzing the first image to determine if any one of the first features of at least one of the plurality of classes indicates that a relevant change has occurred in relation to an object represented thereby.

Abstract: Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating two or more chip images (904) using image data defining a first image (508); concurrently displaying an array (906) comprising all or a portion of the chip images in a plug-in window (702); and displaying in the plug-in window information relating to an attribute (a1, a2) of a feature (A5) contained in a selected one of the displayed chip images (1002). The chip images are generated in response to a user selection of a feature (A1) contained in at least a portion of the first image displayed in an application window (504). Each of the chip images comprises a panned view, a zoomed view, or a panned-and-zoomed view of the first image including one or more features of a user-selected feature class.

Abstract: Systems (100) and methods (300) for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.

Abstract: Systems (100) and methods (300, 400) for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images (1011, 1050, 1231, 1539, 0001, 0102, 0900, 1678, 0500, 0020, 0992, 1033, 1775, 1829) based on at least one first attribute thereof; generating a screen page (1102-1106) comprising an array (1206) defined by a plurality of cells (1208) in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window (802) of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.

Abstract: Systems (100) and methods (300) for efficient video analysis. The methods involve: automatically identifying features of at least one feature class which are contained in a first video stream; simultaneously generating a plurality of first video chips (904) using first video data defining the first video stream; displaying an array comprising the first video chips within a graphical user interface window; and concurrently playing the first video chips. Each of the first video chips comprises a segment of the first video stream which includes at least one identified feature.

Abstract: Systems (100) and methods (300, 3200, 3500) for analyzing topographical models. The methods involve receiving a user input selecting a first content type from a plurality of content types. In response to the reception of the user input, First Model Chips (“FMCs”) are simultaneously generated using terrain elevation data defining a First Topographical Model (“FTM”). Each FMC (1304) comprises at least one of a panned-only view, a zoomed-only view, and a panned-and-zoomed view of FTM (400, 500, 3304, 3306) including at least one item (402-406, 1310) of the first content type that is different from all other items of all other FMCs. Thereafter, a first screen page (1302, 3400) is displayed on a display screen of a computing device. The first screen page comprises a first array (1306, 3404) defined by a plurality of first cells (1308, 3406). Each of the first cells has one of FMCs presented therein.

Abstract: Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating chip images using image data defining at least a first image and video chips using video data defining at least a first video stream. Thereafter, an array is displayed which comprises grid cells in which at least a portion of the chip images is presented, at least a portion of the video chips is presented, or a portion of the chip images and a portion of the video chips are presented. Each chip image comprises a panned-only view, a zoomed-only view, or a panned-and-zoomed view of the first image including a visual representation of at least one first object of a particular type. Each of the video chips comprises a segment of the first video stream which include a visual representation of at least one second object of the particular type.

Abstract: Method for improving the quality of a set of a three dimensional (3D) point cloud data representing a physical surface by detecting and filling null spaces (606). The method includes analyzing (206) the data to identify the presence of a plurality of level 1 fractals (401), each defined by a plurality of voxels (400) containing data points arranged in one of a plurality of three-dimensional patterns. The method also includes selectively filling (212) voxels in the 3D point cloud data with a first predetermined limited number of data points to increase a number of instances where level 2 fractals (604) can be used for representing the 3D point cloud data. Each level 2 fractal is defined as a common plurality of the level 1 fractals having a common three-dimensional pattern, where the common plurality of level 1 fractals are also arranged in accordance with the common three dimensional pattern.

Abstract: A geospatial modeling system may include a geospatial model data storage device, a user input device, and a display. A processor may be included for cooperating with the geospatial model data storage device, the user input device and the display for displaying a geospatial model data set on the display including at least one group of building data points, and displaying a plurality of user-selectable different building shapes on the display based upon the at least one group of building data points. The plurality of user-selectable different building shapes may have different respective feature detail levels. The processor may further replace the at least one group of building data points with a given one of the user-selectable different building shapes based upon user selection thereof with the user input device.

Abstract: Systems (100) and methods (300) for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.

Abstract: Systems (100) and methods (300) for efficiently and accurately detecting changes in feature data. The methods generally involve: determining first vectors for first features extracted from a first image using pixel information associated therewith; comparing the first vectors with second vectors defined by spatial feature data; classifying the first features into a plurality of classes based on the results of the vector comparisons; and analyzing the first image to determine if any one of the first features of at least one of the plurality of classes indicates that a relevant change has occurred in relation to an object represented thereby.

Abstract: Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating two or more chip images (904) using image data defining a first image (508); concurrently displaying an array (906) comprising all or a portion of the chip images in a plug-in window (702); and displaying in the plug-in window information relating to an attribute (a1, a2) of a feature (A5) contained in a selected one of the displayed chip images (1002). The chip images are generated in response to a user selection of a feature (A1) contained in at least a portion of the first image displayed in an application window (504). Each of the chip images comprises a panned view, a zoomed view, or a panned-and-zoomed view of the first image including one or more features of a user-selected feature class.

Abstract: A geospatial modeling system may include a geospatial model data storage device and a processor cooperating with the geospatial model data storage device for inpainting seam-smoothed, void-fill data into a void in a geospatial data set for a geospatial region. The processor may select raw void-fill data from the geospatial data set, and generate the seam-smoothed, void-fill data by applying Poisson's equation to the raw void-fill data using boundary conditions based upon data along a corresponding interface between the void region and adjacent portions of the geospatial region.

Abstract: A geospatial modeling system may include a geospatial model data storage device and a processor. The processor may cooperate with the geospatial model data storage device for identifying a plurality of localized error regions within a geospatial model data set, calculating an overall error value for the geospatial model data set, and inpainting at least one of the localized error regions and re-calculating the overall error value, and stopping inpainting when the overall error value is below an error threshold.

Abstract: A geospatial modeling system may include a geospatial model data storage device and a processor cooperating therewith for determining a void within a geospatial model data set defining a void boundary region, and selecting at least one raw fill region from within the geospatial model data set for filling the void. The processor may also cooperate with the geospatial model data storage device for adjusting elevation values of the at least one raw fill region based upon elevation differences between corresponding portions of the void boundary region and the at least one raw fill region, and updating the geospatial model based upon the adjusted elevation values of the at least one raw fill region.

Abstract: A geospatial modeling system may include a geospatial model database and a processor cooperating with the geospatial model database. The processor may be configured to determine void regions in a geospatial data set including foliage data points and bare earth data points, where each void region has a boundary and at least one bare earth data point therein. The processor may also be configured to inpaint additional bare earth data points into each void region based upon bare earth data points outside the boundary and the at least one bare earth data point therein.