Deep learning in geodesy

Deep learning (DL) can be a way to automate the analysis of predictions. DL algorithms are in the hierarchy of increasing complexity and abstraction while traditional machine learning is linear. In this study, the downscaling of the GRACE-FO satellite was investigated using a convolutional neural network (CNN). Three solutions were used for downscaling with CNN. Down-scaling accuracy is estimated to be 0.1 degree and its absolute error is 1 mm. The results show that this method can improve the GRACE-FO spatial resolution problem with higher efficiency and make it easier to analyze the results. Also, DL can solve many geodesy problems.