ALiDAn: Spatiotemporal and Multiwavelength Atmospheric Lidar Data Augmentation

Adi Vainiger, Omer Shubi, Yoav Y. Schechner, Zhenping Yin, Holger Baars, Birgit Heese, Dietrich Althausen

Research output: Contribution to journalArticlepeer-review


Methods based on statistical learning have become prevalent in various signal processing disciplines and have recently gained traction in atmospheric lidar studies. Nonetheless, such methods often require large quantities of annotated or resolved data. Such data are rare and require effort, especially when exploring evolving phenomena. Existing simulators and databases primarily focus on atmospheric vertical profiles. We propose the Atmospheric Lidar Data Augmentation (ALiDAn) framework to fill this gap. ALiDAn serves as an end-to-end generation and augmentation framework of spatiotemporal and multiwavelength resolved lidar simulated data. ALiDAn employs a hybrid approach of physical models, data statistics, and sampling processes. In addition, it takes into account geographical and seasonal characteristics of aerosols and meteorological conditions along with short- and long-term phenomena that affect lidar measurements. This approach can provide diversified data and robust benchmarks to assist in developing and validating new lidar processing algorithms. We demonstrate simulations compatible with a pulsed time-of-flight lidar. Our approach leverages a broader use of existing databases and can inspire similar data augmentation to other types of lidars and active sensors.

Original languageEnglish
Article number5705517
JournalIEEE Transactions on Geoscience and Remote Sensing
StatePublished - 2022


  • Atmospheric lidar
  • data augmentation
  • data-driven models
  • lidar simulations and databases
  • statistical learning

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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