Urban street tree dataset for image classification and instance segmentation

Abstract

Tree species identification and tree organ segmentation using images are challenging problems that are useful in many forestry-related tasks. In this paper, the urban street tree dataset is proposed as a comprehensive, publicly available dataset covering 50 tree species that contains 41,467 high-resolution classification images (22872 annotated images) from 10 city scenes. Our dataset includes leaf, tree, trunk, branch and trunk (hereafter referred to as branch), flower and fruit subdatasets that were captured under various light intensity, seasonal and shooting conditions. Annotations were performed in a fine-grained manner by using polygons to outline individual objects. We assessed the performance of various vision algorithms on different classification and segmentation tasks, including tree species identification and instance segmentation. Details on the urban street tree dataset are available at https://ytt917251944.github.io/dataset_jekyll.