A geospatial grid is a uniform 2D grid mapped to the earth’s surface. Because the earth is a lumpy 3D object, any 2D grid involves approximating the earth (such as to an ellipsoid) and results in distortion. A variety of map projections are available, and pointers are given on choosing the appropriate map projection to handle trade-offs in the type of distortion associated with each projection. To analyze multiple geospatial grids, it is necessary to remap them to a common 2D grid. This process, illustrated for the Lambert to cylindrical equidistant case, typically involves bilinear interpolation of input grid values. Many image processing operations, like bilinear interpolation, assume that the grid values are locally linear. This has to be verified, either informally using a perceptual color map or formally by testing the root mean square of leave-one-out linear interpolation at different distances. Often geospatial grids have to be created from nonuniform 2D arrays such as from an instrument, from vector graphics such as lines or polygons or by interpolating between point observations. Techniques to handle these cases are described.
KeywordsRoot Mean Square Error Point Observation Spatial Grid Image Processing Operation Output Grid
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