  Pollen grain classification has a remarkable role in many fields from
medicine to biology and agronomy. Indeed, automatic pollen grain classification
is an important task for all related applications and areas. This work presents
the first large-scale pollen grain image dataset, including more than 13
thousands objects. After an introduction to the problem of pollen grain
classification and its motivations, the paper focuses on the employed data
acquisition steps, which include aerobiological sampling, microscope image
acquisition, object detection, segmentation and labelling. Furthermore, a
baseline experimental assessment for the task of pollen classification on the
built dataset, together with discussion on the achieved results, is presented.
