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

2026-02-22

AI Summary

This study addresses a critical gap in urban forestry research—the absence of a large-scale, geographically diverse, and publicly available benchmark dataset for fine-grained street tree classification. To this end, the authors present the first global-scale benchmark for street tree recognition, comprising 12 million images spanning over 8,300 tree species across 133 countries. The dataset incorporates expert validation and a four-level hierarchical taxonomy, covering urban areas on five continents. The work systematically evaluates the performance limitations of state-of-the-art vision models under challenging conditions, including high inter-class similarity, long-tailed species distributions, seasonal variations, and complex imaging environments. Strong baseline metrics are established, offering a foundational resource and new challenges for advancing urban tree management and ecosystem services research.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Object Detection & Categorization
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

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.

Problem

• fine-grained classification
• street trees
• benchmark dataset
• urban ecosystem
• computer vision

Innovation

• fine-grained classification
• street tree dataset
• hierarchical taxonomy
• urban computer vision
• long-tailed recognition