ALiDAn: Spatiotemporal and Multiwavelength Atmospheric Lidar Data Augmentation

Abstract

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.

Omer Shubi

PhD Candidate Data Science

My research revolves around decoding our cognitive state in language comprehension, with a focus on utilizing eye movements in reading, and neural activity while listening.