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Related Experiment Video

Updated: Jul 5, 2025

Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System

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Beyond Conventional Monitoring: A Semantic Segmentation Approach to Quantifying Traffic-Induced Dust on Unsealed

Asanka de Silva¹, Rajitha Ranasinghe¹, Arooran Sounthararajah¹

¹ARC Industrial Transformation Research Hub (ITRH)-SPARC Hub, Department of Civil Engineering, Monash University, Clayton Campus, Clayton, VIC 3800, Australia.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This study introduces a novel deep learning method, semantic segmentation, to quantify traffic-induced road dust. This approach offers a pragmatic and robust alternative to expensive traditional dust monitoring methods.

Keywords:

deep learning dust dust monitoring dust quantification machine learning road dust semantic segmentation traffic-induced dust unsealed roads

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Area of Science:

Environmental Science
Computer Science
Engineering

Background:

Road dust poses significant health risks and environmental concerns.
Current dust monitoring methods are costly and require specialized expertise.
Traffic-induced dust emission impacts road infrastructure and user safety.

Purpose of the Study:

To develop a novel, pragmatic, and robust method for quantifying traffic-induced road dust.
To leverage deep learning, specifically semantic segmentation, for pixel-wise dust identification.
To validate the effectiveness of the proposed method against real-world dust measurements.

Main Methods:

Utilized pre-selected, high-performing semantic segmentation machine learning models.
Applied models for pixel-wise identification of road dust in images.
Correlated the count of identified dust pixels with data from research-grade dust monitors.

Main Results:

Demonstrated that semantic segmentation can reasonably quantify traffic-induced dust.
Achieved over 90% accuracy in predicting low dust concentrations (true positive quadrant).
Validated the method for real-time application and made the code publicly available.

Conclusions:

Semantic segmentation presents a viable and accurate approach for road dust quantification.
The developed method offers a cost-effective and accessible alternative to traditional monitoring techniques.
The study provides a foundation for real-time road dust monitoring and management systems.