StreetTree: A Large-Scale Global Benchmark for Fine-Grained Tree Species Classification

Abstract

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 significantly 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 substantial 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 visual models, we establish strong 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 the refined management and research of urban street trees, while also driving new advancements at the intersection of computer vision and urban science.

Figures (8)

• Figure 1: Long-tail distribution across genus and species level in StreetTree dataset.
• Figure 2: Examples of the binary classification of street view images. Images with less than 20% “tree” class pixels are labeled as negative samples, whereas those exceeding 20% are labeled as positive samples. Segmentation masks of “tree” class are highlighted with green color.
• Figure 2: Example of occlusions caused by street infrastructures.
• Figure 3: Statistics of StreetTree dataset. (a)–(d) show distributions across the four taxonomic levels: Order, Family, Genus, and Species. The horizontal axis uses a logarithmic scale to represent the magnitude of occurrence counts; (e) presents the seasonal distribution; (f) illustrates the temporal span distribution of individual trees.
• Figure 3: Example of excessively strong or weak lighting conditions. (a) and (b) illustrate cases of excessively strong lighting, while (c) and (d) show cases of insufficient illumination.