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Based on digital line graphic and true digital orthophoto map, a large amount of sample data that meets the requirements of deep learning can be automatically generated. However, there are often some erroneous information, which increases the difficulty of training neural network models and limits the improvement of ground feature extraction accuracy. A sample data automatic cleaning method based on selection weight iteration was proposed to address this issue. Firstly, a deep neural network model for data cleaning was constructed, and a network training method based on selection weight iteration was proposed. The method broke the assumption that all samples had the same weight for the calculation of loss function during network model training. The prediction accuracy of the samples during the data cleaning network model training process was used as the weight of the samples to be brought into the network training, and the sample weights were continuously updated through iterative training. Finally, samples with low weights were eliminated to achieve automatic data cleaning and sample database refinement. Training and accuracy comparison experiments were conducted on five classic semantic segmentation network models using a sample database before and after data cleaning. The results show that, the model trained using the sample database after data cleaning improves the average accuracy of building extraction by 2.36%, road extraction by 3.48%, and water extraction by 1.88%. This experiment proves that the data cleaning method proposed in this paper can effectively improve the accuracy of the network model in extracting ground features. [ABSTRACT FROM AUTHOR]

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