Abstract: Objectives:  Digital watermarking technology provides a new means for the security protection of vector map. Existing researches focus mainly on the watermark embedding process, while the watermark detection process is typically the inverse one of the watermark embedding process, and there is a lack of self-optimization methods of watermark extraction results. Consequently, there is still much upgrade space for the watermark detection effect. A self-correcting digital watermarking model is designed for vector map based on error-control coding of copyright watermark information.   Methods:  Error-correction coding (ECC) and Cyclic Redundancy Checking (CRC) are performed on the original copyright watermark data firstly. The original watermark data and the ECCs and CRC codes are then treated by lossless compression coding, i.e. Huffman Coding (HC), so that the watermark length can be constrained. The mixed watermark data with self-correcting ability is then generated by combining the results of HC. Afterwards, a differentiated embedding method is proposed for the various components of the generated mixed watermark data, considering their different characteristics and the stability difference of map vertices. After watermark extraction, Huffman Decoding (HD) is firstly performed on the extracted results and then the copyright watermark data, the ECCs and the CRC codes can be obtained by separation. Afterwards, partial error bits in the obtained copyright watermark data can be detected and corrected based on the obtained ECCs and CRC codes, and the detection effect of copyright watermark can be further improved.   Results:  Experimental results show that the proposed method can improve further the detection effect of copyright watermark based on previous research, and it has ideal reversibility, invisibility, robustness and watermark capacity.   Conclusions:  Although the constructed digital watermarking model is quite robustness against conventional transformations and attacks, it is very fragile under map scaling. Therefore, it is worthy of further study on the construction of geometric invariants of vector map data, as well as the corresponding watermark embedding methods.