AI Assisted Data Selection of Laser Altimeter Observations

Laser altimeters create large amounts of data that often have to be preprocessed and checked before further use. The BepiColombo mission to Mercury is set to arrive in December 2025 and observations with the BepiColombo Laser Altimeter (BELA, (Benkhoff et al., 2010; Thomas et al., 2021)) will start during the following spring. These measurements are planned to be used to derive information about the tides of Mercury (Thor et al., 2020). Careful assessment, selection, and filtering on the raw data is needed to extract the small tidal signal. Until the BELA data becomes available artificial data and records from other missions have to be used to study the data selection strategy. We present our work on MESSENGER Laser Altimeter (MLA, (Cavanaugh et al., 2007)) using a convolutional neural network to sort observations on an orbit by orbit basis into different classes. The already existing neural network (Stenzel and Hilchenbach, 2021; Stenzel, Thor and Hilchenbach, 2021) is tuned and a new test data set is created.

 

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Stenzel, O. and Hilchenbach, M. (2021) ‘Towards machine learning assisted error identification in orbital laser altimetry for tides derivation’, pp. EPSC2021-688. Available at: https://doi.org/10.5194/espc2021-688.

Stenzel, O., Thor, R. and Hilchenbach, M. (2021) ‘Error identification in orbital laser altimeter data by machine learning’, pp. EGU21-14749. Available at: https://doi.org/10.5194/egusphere-egu21-14749.

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