We introduce an automated approach for generating segmentation masks for citizen science plant photos, making them applicable to computer vision models. This framework effectively transforms citizen science data into a data treasure for segmentation models for plant species identification in aerial imagery. Using automatically labeled photos, we train segmentation models for mapping tree species in drone imagery, showcasing their potential for forestry, agriculture, and biodiversity monitoring.