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Updated: May 15, 2026

$Femtosecond Laser Filaments for Use in Sub-Diffraction-Limited Imaging and Remote Sensing$

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Airborne Particulate Matter Sensing via Laser Filament-Interaction and Deep Learning.

James A Grant-Jacob¹, Ben Mills¹

¹Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, United Kingdom.

|May 14, 2026

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Remote sensing of airborne particles like pollen is now possible. Femtosecond laser technology combined with AI accurately identifies airborne particulates in real-time, improving environmental monitoring.

Area of Science:

Atmospheric Science
Laser Physics
Artificial Intelligence

Background:

Airborne particulate matter poses significant risks to public health, agriculture, and environmental monitoring.
Existing technologies cannot identify individual airborne particles remotely in real-time.
There is a need for advanced methods for real-time atmospheric particulate analysis.

Purpose of the Study:

To demonstrate the feasibility of remote, real-time identification of airborne particulates.
To develop a system combining femtosecond laser filamentation, optical imaging, and deep learning for particle analysis.
To assess the accuracy of this system in classifying different types of airborne particles.

Main Methods:

Airborne particles (chalk dust, pollen, salt crystals) were introduced into a femtosecond laser-generated filament.

Keywords:

airborne filamentation lasers particulates remote sensing scattering

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Optical emission events from individual particles within the filament were captured.

A lightweight convolutional neural network was trained using deep learning to classify particle types based on optical emissions.

Main Results:

The developed system achieved high classification accuracy for airborne particulates.
The mean accuracy across all tested categories (chalk, pollen, salt) was 87.5%.
Deep learning model visualizations confirmed the focus on discriminative spectral and spatial features for accurate identification.

Conclusions:

Femtosecond laser filamentation with optical imaging and deep learning enables remote, species-level airborne particulate detection.
This technology provides a foundation for developing intelligent, real-time atmospheric sensing platforms.
The study highlights a significant advancement in environmental monitoring and public health protection from airborne threats.