MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall

• Published in: Scientific Data
• Main Authors: Grazioli, Jacopo, Ghiggi, Gionata, Billault-Roux, Anne-Claire, Berne, Alexis
• Format: Text
• Language: unknown
• Published: 2022
• Subjects: Triplets; Antarc*; Antarctica
• Online Access: http://infoscience.epfl.ch/record/294098; https://doi.org/10.1038/s41597-022-01269-7; https://infoscience.epfl.ch/record/294098/files/s41597-022-01269-7.pdf

Snowfall information at the scale of individual particles is rare, difficult to gather, but fundamental for a better understanding of solid precipitation microphysics. In this article we present a dataset (with dedicated software) of in-situ measurements of snow particles in free fall. The dataset includes gray-scale (255 shades) images of snowflakes, co-located surface environmental measurements, a large number of geometrical and textural snowflake descriptors as well as the output of previously published retrieval algorithms. These include: hydrometeor classification, riming degree estimation, identification of melting particles, discrimination of wind-blown snow, as well as estimates of snow particle mass and volume. The measurements were collected in various locations of the Alps, Antarctica and Korea for a total of 2'555'091 snowflake images (or 851'697 image triplets). As the instrument used for data collection was a Multi-Angle Snowflake Camera (MASC), the dataset is named MASCDB. Given the large amount of snowflake images and associated descriptors, MASCDB can be exploited also by the computer vision community for the training and benchmarking of image processing systems.