Drive-by Urban Freight Sensing: Large-Scale Fine-Grained Truck Classification Using Google Street View Imagery and Deep Learning
TL;DR
A novel drive-by sensing framework using Google Street View imagery (GSV) combined with advanced computer vision and deep learning methods to generate fine-grained truck classification data at scale in urban areas and demonstrates the potential of computer vision and drive-by sensing technologies for scalable urban freight monitoring and environmental impact assessment.
Abstract
Trucks are essential to urban economies through facilitating the movement of goods and services, but their presence significantly impacts urban infrastructure, traffic congestion, air quality, and noise pollution. Effective urban freight monitoring and emission estimation require a detailed understanding of urban truck distribution by their classes at scale. However, obtaining such data remains a significant challenge, especially in complex urban settings. This study presents a novel drive-by sensing framework using Google Street View imagery (GSV) combined with advanced computer vision and deep learning methods to generate fine-grained truck classification data at scale in urban areas. The framework consists of four main components: first, a customized YOLO-based detector for truck detection and classification from GSV imagery; second, Depth Anything V2 integration for RGB-based depth estimation to filter out distant and less visible objects; third, EfficientNet for fine-grained truck classification; and fourth, a case study on census tract-level correlation analysis between detected truck distribution and environmental data to demonstrate the effectiveness of the drive-by sensing approach. On held-out, panorama-disjoint, geographically blocked test sets, the detector achieves 65.2% precision and 56.9% recall, and fine-grained classification reaches 80.0% overall accuracy (macro-F1 = 0.59, averaged over ten training seeds) across seven commodity-informed truck categories. The GSV-based approach provides extensive spatial coverage for large-scale urban freight monitoring. A case study in Manhattan demonstrates the framework’s capability to identify spatial patterns in truck distribution, with an exploratory census tract-level analysis ( $N$ = 90 tracts) revealing a positive association between near-field truck prevalence and PM2.5 concentrations that persists under spatial regression models accounting for spatial autocorrelation. This research demonstrates the potential of computer vision and drive-by sensing technologies for scalable urban freight monitoring and environmental impact assessment.