Abstract: A method is provided for setting, by a processor, a minimum and maximum symbol size for rendering at different zoom levels on a digital map. In another embodiment of the invention, the size of overlay features, such as line widths, received by a processor is automatically adjusted for different zoom levels depending on the average size of map features, such as polygons. In one example, line widths are decreased, and/or made partially transparent, as the map views are zoomed out, finally being altogether eliminated. In one embodiment, the system automatically analyzes received data to determine an appropriate way to map the data.

Abstract: Certain embodiments include a computer-implemented method that includes receiving image data corresponding to a three-dimensional (3D) parametric model, receiving a selection of a scope of the 3D model, receiving a selection of one or more base lines for the scope, determining a slip type for the scope, calculating planes of the scope, calculating candidate dimension lines, selecting a dimension line of the candidate dimension lines based on dimension line placement rules, and displaying the selected dimension line adjacent to the scope that correspond to the selected one or more base lines. The calculated planes of the scope can be based on the one or more base lines and the slip type. The calculated candidate lines can be based on the calculated planes and dimension line placement guidelines.

Abstract: Embodiments of the invention relate to an image or raster compression method that includes receiving pixel data for a raster comprising a two dimensional (2D) array of pixels where each pixel is associated with a data value. The method further includes receiving a user defined parameter defining a maximum error allowable per pixel for a compression algorithm. The raster can be divided into a number of pixel blocks where each pixel can be quantized and bit stuffed based on a number of block statistics including the maximum error allowable. The method further includes executing the compression algorithm wherein for each pixel, where an error caused by the compression algorithm is equal to or less than the maximum error allowable, and encoding the pixel data based on the compression algorithm. In certain embodiments, the compression algorithm is a non-transform compression algorithm.

Abstract: A computer-implemented method includes providing a user interface on an internet-protocol (IP) connected mobile device, the user interface configured to receive a user input corresponding to one or more data privacy parameters for geo-location data, and controlling a transferring of geo-location data to and from each of a plurality of mobile applications on the mobile device based on the user input. A change in one or more of the data privacy parameters can change how geo-location data is provided to each of the plurality of applications and can affect location data accuracy, location data reporting frequency, geo-functions, and more. The user interface can be configured to allow a user to view, manage, and delete a personal location history. Furthermore, one or more profiles can be associated with one or more of the plurality of mobile applications, where each of the one or more profiles is assigned individual data privacy parameters.

Abstract: Certain embodiments of the invention relate to a computer-implemented method that comprises analyzing an image over a plurality of different scales, where the analyzing includes determining spectral characteristics of the image at each of the plurality of different scales, determining spatial characteristics of the image at each of the plurality of different scales, and determining a segmentation pattern (i.e., superpixel) for the image at each of the plurality of different scales based on the spectral and spatial characteristics of that particular scale. The method further includes identifying objects in the image based on portions of the segmentation patterns that are scale-invariant over the plurality of different scales. In some cases, the method can include determining statistical, textural, and/or intensity characteristics of the image at each scale, where determining the segmentation patterns for the image at each scale is further based on one or more of these characteristics.

Abstract: Embodiments of the invention are directed to a computer-implemented system and method of identifying human settlements in imagery comprising receiving an image, segmenting the image into a plurality of superpixels, analyzing statistical parameters of at least two or more of the plurality of superpixels, where the statistical parameters includes entropy data, and identifying groups of superpixels having at least a predetermined cluster density and a predetermined entropy. Some embodiments further include clipping the image to only include the identified groups of superpixels having the predetermined cluster density and entropy, analyzing statistical parameters of the clipped image, analyzing geometric factors of the clipped image, determining one or more settlements based on the statistical parameters and geometric factors of the superpixels, and identifying a shape and area of the one or more settlements based on the statistical parameters and geometric factors of the clipped image.

Abstract: Embodiments of the invention relate to an image or raster compression method that includes receiving pixel data for a raster comprising a two dimensional (2D) array of pixels where each pixel is associated with a data value. The method further includes receiving a user defined parameter defining a maximum error allowable per pixel for a compression algorithm. The raster can be divided into a number of pixel blocks where each pixel can be quantized and bit stuffed based on a number of block statistics including the maximum error allowable. The method further includes executing the compression algorithm wherein for each pixel, where an error caused by the compression algorithm is equal to or less than the maximum error allowable, and encoding the pixel data based on the compression algorithm. In certain embodiments, the compression algorithm is a non-transform compression algorithm.

Abstract: A computer-implemented method includes providing a user interface on an internet-protocol (IP) connected mobile device, the user interface configured to receive a user input corresponding to one or more data privacy parameters for geo-location data, and controlling a transferring of geo-location data to and from each of a plurality of mobile applications on the mobile device based on the user input. A change in one or more of the data privacy parameters can change how geo-location data is provided to each of the plurality of applications and can affect location data accuracy, location data reporting frequency, geo-functions, and more. The user interface can be configured to allow a user to view, manage, and delete a personal location history. Furthermore, one or more profiles can be associated with one or more of the plurality of mobile applications, where each of the one or more profiles is assigned individual data privacy parameters.

Abstract: Embodiments relate to a computer-implemented method comprising a process of receiving geographic data corresponding to a geographic area, receiving population data corresponding to a population of the geographic area, and generating a distribution of the population over the geographic area based on characteristics of geographic features of the geographic area. Characteristics of the geographic features can be classified into one or more usage categories corresponding to an estimated populated density associated with the particular geographic feature. A grid can be generated to overlay the geographic area, and the distribution of the population to an area encapsulated within each of the plurality of grid cells can be interpolated based on the usage categories of the geographic features and a position of the geographic features with respect to each of the plurality of grid cells.

Abstract: Embodiments of the invention relate to an image or raster compression method that includes receiving pixel data for a raster comprising a two dimensional (2D) array of pixels where each pixel is associated with a data value. The method further includes receiving a user defined parameter defining a maximum error allowable per pixel for a compression algorithm. The raster can be divided into a number of pixel blocks where each pixel can be quantized and bit stuffed based on a number of block statistics including the maximum error allowable. The method further includes executing the compression algorithm wherein for each pixel, where an error caused by the compression algorithm is equal to or less than the maximum error allowable, and encoding the pixel data based on the compression algorithm. In certain embodiments, the compression algorithm is a non-transform compression algorithm.