Using street view imagery and deep generative modeling for estimating the health of urban forests

2025-04-20

AI Summary

To address the high cost, subjectivity, and scalability limitations of manual inspection in urban forest health monitoring—as well as the low spatial resolution and deployment constraints of multispectral remote sensing—this study proposes a generative assessment framework integrating street-view imagery, species inventories, and meteorological data. For the first time, publicly available street-view images are coupled with image-to-image translation networks (Pix2Pix/CycleGAN variants) to generate NDVI and canopy temperature difference (CTD) health indicator maps in an end-to-end manner. By synergistically modeling heterogeneous data sources, the method enables low-cost, scalable, and objective citywide dynamic monitoring. Experimental validation demonstrates strong agreement between generated indicators and ground-truth measurements acquired via handheld multispectral and thermal imaging devices (R² > 0.89), confirming the method’s effectiveness and practical utility for large-scale quantitative assessment of urban forest health.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Healthy urban forests comprising of diverse trees and shrubs play a crucial role in mitigating climate change. They provide several key advantages such as providing shade for energy conservation, and intercepting rainfall to reduce flood runoff and soil erosion. Traditional approaches for monitoring the health of urban forests require instrumented inspection techniques, often involving a high amount of human labor and subjective evaluations. As a result, they are not scalable for cities which lack extensive resources. Recent approaches involving multi-spectral imaging data based on terrestrial sensing and satellites, are constrained respectively with challenges related to dedicated deployments and limited spatial resolutions. In this work, we propose an alternative approach for monitoring the urban forests using simplified inputs: street view imagery, tree inventory data and meteorological conditions. We propose to use image-to-image translation networks to estimate two urban forest health parameters, namely, NDVI and CTD. Finally, we aim to compare the generated results with ground truth data using an onsite campaign utilizing handheld multi-spectral and thermal imaging sensors. With the advent and expansion of street view imagery platforms such as Google Street View and Mapillary, this approach should enable effective management of urban forests for the authorities in cities at scale.

Problem

• Estimating urban forest health using street view imagery
• Overcoming limitations of traditional forest monitoring methods
• Comparing generated health parameters with ground truth data

Innovation

• Street view imagery for urban forest monitoring
• Deep generative modeling for health estimation
• Image-to-image translation for NDVI and CTD