Machine learning methods for LIDAR measurements: review

D. A. Vrazhnov; A. I. Knyazkova; Y. V. Kistenev; O. A. Romanovskii

doi:10.1117/12.2692926

17 October 2023 Machine learning methods for LIDAR measurements: review

D. A. Vrazhnov, A. I. Knyazkova, Y. V. Kistenev, O. A. Romanovskii

Proceedings Volume 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 127803T (2023) https://doi.org/10.1117/12.2692926
Event: XXIX International Symposium "Atmospheric and Ocean Optics, Atmospheric Physics", 2023, Moscow, Russian Federation

Abstract

Machine learning methods have found applications in areas such as security, molecular biology, medicine, computational physics and mechanics, etc. In this paper a review on using machine learning technologies for LIDAR measurements is present based on Google Scholar, Scopus, and Web of Science citing databases. Search includes keywords “lidar”, “atmospheric sensing”, “machine learning” through past 5 years. Most relevant and significant papers were selected.

Citation Download Citation

D. A. Vrazhnov, A. I. Knyazkova, Y. V. Kistenev, and O. A. Romanovskii "Machine learning methods for LIDAR measurements: review", Proc. SPIE 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 127803T (17 October 2023); https://doi.org/10.1117/12.2692926

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