Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering

TL;DR

This paper presents an unsupervised visual clustering method that combines street-level imagery and spatial data to accurately map urban tree biodiversity across cities without needing labeled datasets.

Contribution

It introduces a novel unsupervised framework integrating visual embeddings and spatial patterns to estimate urban biodiversity, overcoming limitations of traditional and supervised methods.

Findings

Accurately recovers genus-level diversity patterns

Achieves low Wasserstein distances to ground truth indices

Preserves spatial autocorrelation in biodiversity estimates

Abstract

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.