The invention discloses a three-dimensional cadastral data
anomaly detection method based on
deep learning, and particularly relates to the field of
data processing, and the method comprises the steps: S1, cadastral unit coding, S2, cadastral
survey result generation, S3, ownership analysis, S4, anomaly
perception, S5, risk analysis, and S6, human-computer interaction. According to the invention, unique identification of each piece of cadastral data is realized through cadastral unit coding, then abnormity
perception is carried out on the cadastral data, resource
perception and abnormal state monitoring are carried out based on the cadastral
survey result, risk analysis of the cadastral data is carried out, a
closed loop of monitoring, analysis and early warning is formed, and the risk of the cadastral data is improved. Therefore, a traditional passive problem
processing mode is optimized, the situation that the large-scale three-dimensional cadastral data
rapid processing requirement cannot be met due to the fact that the
data processing efficiency is limited is avoided, the
anomaly detection efficiency is improved when normal cadastral data collection is guaranteed, and a safe and credible tool is provided for unified management of the cadastral data.