Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-391-2021
https://doi.org/10.5194/amt-14-391-2021
Research article
 | 
18 Jan 2021
Research article |  | 18 Jan 2021

Classification of lidar measurements using supervised and unsupervised machine learning methods

Ghazal Farhani, Robert J. Sica, and Mark Joseph Daley

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Robert Sica on behalf of the Authors (13 Oct 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (21 Oct 2020) by Joanna Joiner
RR by Benoît Crouzy (09 Nov 2020)
ED: Publish as is (13 Nov 2020) by Joanna Joiner
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Short summary
While it is relatively straightforward to automate the processing of lidar signals, it is difficult to automatically preprocess the measurements to distinguish between good and bad scans. It is easy to train humans to perform the task; however, considering the growing number of measurements, it is a time-consuming, on-going process. We have tested some machine learning algorithms for lidar signal classification and had success with both supervised and unsupervised methods.