Research on Large-Area Coordinate Transformation Method Based on Cluster Iterative Partition

In order to solve the problem that the control range of existing coordinate transformation models is limited and the precision is insufficient in the case of large area, a large-area coordinate transformation method based on cluster iterative partition is proposed in this paper. The method uses K-means clustering algorithm to carry out multi-level iterative selection of control points, then determines the control range by using the idea of KNN algorithm, uses the range threshold to determine the partitioning strategy, and converts the coordinate transformation of a large area into the coordinate transformation of multiple small areas, effectively solving the problem of insufficient control range. Finally, an example is given to verify the feasibility of the method, which not only satisfies the precision of large-area coordinate transformation, but also applies to the region division of various distribution cases.