The fine-grained classification of street trees is a crucial task for urban planning, streetscape management, and the assessment of urban ecosystem services. However, progress in this field has been hindered by the lack of large-scale, geographically diverse, and publicly available benchmark datasets specifically designed for street trees. To address this critical gap, we introduce StreetTree, the world’s first large-scale benchmark dataset dedicated to fine-grained street tree classification. The dataset contains over 12 million images covering more than 8,300 common street tree species, collected from urban streetscapes across 133 countries spanning five continents, and supplemented with expert-verified observational data. StreetTree poses challenges for pretrained vision models under complex urban environments: high inter-species visual similarity, long-tailed natural distributions, significant intra-class variations caused by seasonal changes, and diverse imaging conditions such as lighting, occlusions from buildings, and varying camera angles. In addition, we provide a hierarchical taxonomy (order–family–genus–species) to support research in hierarchical classification and representation learning. Through extensive experiments with various vision models, we establish solid baselines and reveal the limitations of existing methods in handling such real-world complexities. We believe that StreetTree will serve as a key resource for driving new advancements at the intersection of computer vision and urban science. The data is now publicly available at <a href="https://doi.org/10.6084/m9.figshare.32172486" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.32172486</a> (Li et al., 2026).