The assessment of wemaps audit requirements based on deep learning

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ABSTRACT As a specialized map product, Wemaps must comply with relevant laws and regulations. Map audit plays a crucial role in ensuring map quality by preventing the production and dissemination of problem maps, as well as safeguarding national sovereignty, security, and interests. The user base for Wemaps is diverse, encompassing various types of maps, vast amounts of map data, and high expectations for timely dissemination. However, the current map audit process is inefficient and burdensome, failing to meet the specific needs of Wemaps audits. The key to solving this problem lies in the ability to automate and rapidly assess the audit requirements of Wemaps, approving those that require audit and promptly releasing those that do not. This study aims to establish an automated Wemaps audit assessment model using convolutional neural networks and transfer learning methods. By doing so, the burden of map audit can be reduced, and dissemination efficiency can be improved. The main contributions of this study are as follows: (1) Establishment of a dataset for assessing Wemaps audit requirements. (2) Utilization of VGG16 and ResNet50 neural network models for assessing Wemaps audit requirements; (3) Development of an optimal Wemaps audit assessment model through various experiments and training methods. (4) Analysis of factors influencing audit assessments based on measurement indicators and visualized results of the model. The experiments demonstrate that this method achieves high accuracy and can provide assessment services for public map audit requirements.