Research on Efficient Information Surveying and Mapping Processing Based on Autoencoder
With the development of Artificial Intelligence(AI)technology,information surveying and mapping is gradually moving towards intelligence.In order to clean information surveying and mapping data,a stacked denois-ing autoencoder was adopted in the research,and particle swarm optimization algorithm was introduced to optimize the hyperparameters in the autoencoder to reduce the impact of hyperparameters on the performance of the stacked denoising autoencoder.The results showed that the relative error percentage,root mean square error,average abso-lute error,and average percentage error of the stacked denoising autoencoder after optimization were 1.06%,0.525%,0.315%,and 0.570%,respectively.This autoencoder can perform better cleaning on surveying data,reduce errors,and improve data quality.
AutoencoderStackingNoise reductionSurveying and mappingData clean
万方数据
维普
