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:
A geospatial modeling system may include a geospatial model database and a processor. The processor may cooperate with the geospatial database for identifying a building roof type defined by building roof data points as being from among a plurality of possible building roof types. This may be done based upon applying multi-directional gradient calculations to the building roof data points.
Abstract:
A geospatial modeling system may include a geospatial model database and a processor. The processor may cooperate with the geospatial model database for inpainting data into at least one void in geospatial model terrain data based upon propagating contour data from outside the at least one void into the at least one void.
Abstract:
A geospatial modeling system may include a geospatial model database and a processor. The processor may cooperate with the geospatial database for identifying a building roof type defined by building roof data points as being from among a plurality of possible building roof types. This may be done based upon applying multi-directional gradient calculations to the building roof data points.
Abstract:
A geospatial modeling system may include a geospatial model database and a processor. The processor may cooperate with the geospatial model database for extracting ground data from foliage and building data, performing at least one noise filtering operation on the foliage and building data including at least one sum of differences operation, and separating foliage data from the building data based upon the at least one noise filtering operation.
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:
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.