用街景图像无监督建模城市树木多样性，省时省力还精准。
Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering
- 融合街景视觉嵌入与空间种植模式，无标签实现多样性聚类。
- 在8个北美城市验证，香农和辛普森指数与真实值差距极小。
- 适合缺乏树木普查数据的城市，支持长期低成本监测。
城市树木多样性对气候韧性、生态稳定和宜居性至关重要，但多数城市缺乏详细的树冠知识。实地调查虽能可靠估算香农和辛普森多样性指数，但成本高、耗时长；而有监督的AI方法依赖标注数据，泛化能力差。本文提出一种无监督聚类框架，将街景图像的视觉嵌入与空间种植模式结合，无需标签即可估计多样性。在8个北美洲城市应用中，该方法准确恢复了属级多样性模式，香农与辛普森指数的沃瑟斯坦距离低，且保持了空间自相关性。这一可扩展、细粒度的方法，使缺乏详细普查数据的城市也能实现生物多样性制图，并为绿色空间公平获取和城市生态系统动态管理提供持续、低成本监测路径。
原文摘要 · Abstract (English)
Urban tree biodiversity is critical for climate resilience, ecological stability, and livability in cities, yet most municipalities lack detailed knowledge of their canopies. Field-based inventories provide reliable estimates of Shannon and Simpson diversity but are costly and time-consuming, while supervised AI methods require labeled data that often fail to generalize across regions. We introduce an unsupervised clustering framework that integrates visual embeddings from street-level imagery with spatial planting patterns to estimate biodiversity without labels. Applied to eight North American cities, the method recovers genus-level diversity patterns with high fidelity, achieving low Wasserstein distances to ground truth for Shannon and Simpson indices and preserving spatial autocorrelation. This scalable, fine-grained approach enables biodiversity mapping in cities lacking detailed inventories and offers a pathway for continuous, low-cost monitoring to support equitable access to greenery and adaptive management of urban ecosystems.
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