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

2025-08-19

AI Summary

Urban areas widely lack high-resolution tree species diversity data, hindering climate resilience enhancement and equitable green infrastructure allocation. To address the high cost of field surveys and poor generalizability of supervised learning approaches, this paper proposes an unsupervised, spatially aware visual clustering framework. It jointly optimizes self-supervised visual embeddings derived from street-level imagery and spatial distribution patterns of trees, integrating visual feature learning with geographic spatial autocorrelation structure—enabling, for the first time, fine-grained, cross-city tree species diversity mapping without labeled training data. The method directly estimates spatial distributions of Shannon and Simpson diversity indices. Evaluated across eight North American cities, it accurately reconstructs ground-truth diversity patterns, achieving low Wasserstein distances. The framework demonstrates strong scalability and practical deployability for urban ecological monitoring.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Estimating urban tree biodiversity without costly field inventories
• Mapping tree diversity patterns across cities using unsupervised methods
• Overcoming generalization issues in supervised AI with spatial-visual clustering

Innovation

• Unsupervised clustering of street imagery
• Integrates visual embeddings with spatial patterns
• Estimates biodiversity without labeled data