DustNet++: Deep Learning-Based Visual Regression for Dust Density Estimation
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11263_2025_Article_2376.pdf
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5.79 MB
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Author(s)
Michel, Andreas
Weinmann, Martin
Kuester, Jannick
AlNasser, Faisal
Gomez, Tomas
Falvey, Mark
Schmitz, Rainer
Middelmann, Wolfgang
Hinz, Stefan
Date Issued
February 24, 2025
Journal
International Journal of Computer Vision
Publisher
Springer US
Citation
Michel, A., Weinmann, M., Kuester, J. et al. DustNet++: Deep Learning-Based Visual Regression for Dust Density Estimation. Int J Comput Vis 133, 4220–4244 (2025).
Version
Final published version
Abstract
Detecting airborne dust in standard RGB images presents significant challenges. Nevertheless, the monitoring of airborne dust holds substantial potential benefits for climate protection, environmentally sustainable construction, scientific research, and various other fields. To develop an efficient and robust algorithm for airborne dust monitoring, several hurdles have to be addressed. Airborne dust can be opaque or translucent, exhibit considerable variation in density, and possess indistinct boundaries. Moreover, distinguishing dust from other atmospheric phenomena, such as fog or clouds, can be particularly challenging. To meet the demand for a high-performing and reliable method for monitoring airborne dust, we introduce DustNet++, a neural network designed for dust density estimation. DustNet++ leverages feature maps from multiple resolution scales and semantic levels through window and grid attention mechanisms to maintain a sparse, globally effective receptive field with linear complexity. To validate our approach, we benchmark the performance of DustNet++ against existing methods from the domains of crowd counting and monocular depth estimation using the Meteodata airborne dust dataset and the URDE binary dust segmentation dataset. Our findings demonstrate that DustNet++ surpasses comparative methodologies in terms of regression and localization capabilities.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s11263-025-02376-9
