FlakeOut: A Geometric Approach to Remove Wind-Blown Snow from Terrestrial Laser Scans

TL;DR

FlakeOut is a novel geometric filtering method specifically designed to effectively remove wind-blown snow particles from terrestrial laser scanning data, significantly reducing false positives compared to standard techniques.

Contribution

The paper introduces FlakeOut, a new filter with a low false positive rate tailored for snow surface data contaminated by wind-blown snow, along with tools to estimate filter false positives.

Findings

FlakeOut achieves a false positive rate of 2.8e-4, much lower than standard methods.

It effectively preserves true ground points in snow-covered terrain.

The method is suitable for quantitative snow surface analysis in windy conditions.

Abstract

Wind-blown snow particles often contaminate Terrestrial Laser Scanning (TLS) data of snow covered terrain. However, common filtering techniques fail to filter wind-blown snow and incorrectly filter data from the true surface due to the spatial distribution of wind-blown snow and the TLS scanning geometry. We present FlakeOut, a filter designed specifically to filter wind-blown snowflakes from TLS data. A key aspect of FlakeOut is a low false positive rate of \num{2.8e-4} -- an order of magnitude lower than standard filtering techniques -- which greatly reduces the number of true ground points that are incorrectly removed. This low false positive rate makes FlakeOut appropriate for applications requiring quantitative measurements of the snow surface in light to moderate blowing snow conditions. Additionally, we provide mathematical and software tools to efficiently estimate the false positive rate of filters applied for the purpose of removing erroneous data points that occur very infrequently in a dataset.