Abstract: A method of fast gridding of irregular data, has been developed for spatial interpolation of large irregular spatial point data sets; for example building a 3D geographic terrain grid surface from billions of irregularly spaced xyz coordinates on the earth's surface. The method developed typically translates into many orders of magnitude gain in computational speed. For example, to produce a gridded data set (having M rows and N columns) from P irregularly located sampling points, the computational steps required can be reduced from a number of the order of O(M×N×P) to a lesser number of the order of O(M×N+P) operations. The method achieves this by ensuring that each of the P sampling points is visited only once. This is particularly significant since spatial data collection devices typically collect data points in the billions. The method described is readily extendible to any number of dimensions.

Abstract: A system and method of providing efficient management of distributed, diversified, large sized spatial data as a scalable solution. The system and method are based on the combination of the following: Distributed spatial data is managed by spatial data servers which are deployed next to each spatial data source that needs to be accessed allowing spatial data to remain in the location where it was created. Spatial data indices allow fast delivery of large spatial data sets and are automatically updated whenever spatial data sets are modified. Spatial meta servers coordinate the spatial data servers, publish the existence of the spatial data, define access permissions and communicate with other spatial meta severs extending the spatial data connectivity into other networks. Spatial data providers deployed next to each location requiring access to the spatial data allow spatial data to be discovered and accessed directly from the spatial data servers.

Abstract: An apparatus and method for aligning a line scan camera with a Light Detection and Ranging (LiDAR) scanner for real-time data fusion in three dimensions is provided. Imaging data is captured at a computer processor simultaneously from the line scan camera and the laser scanner from target object providing scanning targets defined in an imaging plane perpendicular to focal axes of the line scan camera and the LiDAR scanner. X-axis and Y-axis pixel locations of a centroid of each of the targets from captured imaging data is extracted. LiDAR return intensity versus scan angle is determined and scan angle locations of intensity peaks which correspond to individual targets is determined. Two axis parallax correction parameters are determined by applying a least squares. The correction parameters are provided to post processing software to correct for alignment differences between the imaging camera and LiDAR scanner for real-time colorization for acquired LiDAR data.