# Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing

arXiv: https://arxiv.org/abs/2212.03936 · HTML: https://mlsanity.com/paper/2212.03936/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [Self-Updating Vehicle Monitoring Framework Employing Distributed Acoustic Sensing towards Real-World Settings](https://mlsanity.com/paper/2409.10259/) (2024)
- [Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs](https://mlsanity.com/paper/2412.12743/) (2024)
- [Physics-informed network paradigm with data generation and background noise removal for diverse distributed acoustic sensing applications](https://mlsanity.com/paper/2506.21952/) (2025)
- [Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning](https://mlsanity.com/paper/2609.28793/) (2026)

## Abstract (arXiv)

Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic cables into a large-scale strain sensing array has shown the potential to revolutionize urban traffic monitoring by providing a fine-grained, scalable, and low-maintenance monitoring solution. However, the real-world application of DAS is hindered by challenges such as noise contamination and interference among closely traveling cars. In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution. Our work extends recent research by introducing three key advancements. Firstly, we perform a comprehensive resolution analysis of DAS-recorded traffic signals, laying a theoretical foundation for our approach. Secondly, we incorporate space-domain vehicle wavelets into our U-Net model, enabling consistent high-resolution outputs regardless of vehicle speed variations. Finally, we employ L-2 norm regularization in the loss function, enhancing our model's sensitivity to weaker signals from vehicles in remote traffic lanes. We evaluate the effectiveness and robustness of our method through field recordings under different traffic conditions and various driving speeds. Our results show that our method can enhance the spatial-temporal resolution and better resolve closely traveling cars. The spatial deconvolution U-Net model also enables the characterization of large-size vehicles to identify axle numbers and estimate the vehicle length. Monitoring large-size vehicles also benefits imaging deep earth by leveraging the surface waves induced by the dynamic vehicle-road interaction.