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: 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 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 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: A geospatial modeling system may include a geospatial database for storing geospatial data points each having respective elevations associated therewith. The system may further include a processor for data thinning the geospatial data points by selecting discriminant points therefrom.

Abstract: A geospatial modeling system may include a geospatial database for storing geospatial data points each having respective elevations associated therewith. The system may further include a processor for data thinning the geospatial data points by selecting discriminant points therefrom.