Spatial embedding
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Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types.[1] representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension.
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Such embedding methods allow complex spatial data to be used in neural networks and have been shown to improve performance in spatial analysis tasks[2][3]