Imagine taking pictures of thousands of snowflakes from three different angles with a specialized instrument installed at an altitude of 2,500 meters. Then imagine using 3,500 of these pictures to manually train an algorithm to recognize six different classes of snowflakes. And, finally, imagine using this algorithm to classify the snowflakes in the millions of remaining pictures into those six classes at breakneck speed. That’s exactly what researchers at EPFL’s Environmental Remote Sensing Laboratory (LTE) did, in a project spearheaded by Alexis Berne. Their pioneering approach was featured in the latest issue of Atmospheric Measurement Techniques.