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Remote Sensing Advances in Urban Traffic Monitoring (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 705

Special Issue Editors


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Guest Editor
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Interests: road traffic control systems; monitoring of road traffic using image processing methods; development ofremote sensing devices using IoT technology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Interests: neural network; deep learning; traffic flow prediction; object classifier; road traffic conditions classification; energy estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching the second Special Issue for Remote Sensing entitled “Remote Sensing Advances in Urban Traffic Monitoring”.

The ongoing process of urban development is exacerbating the problems related to controlling and managing traffic in road networks. The basis for an efficient solution to these issues is the accurate and timely collection of traffic data. The creation of reliable traffic data banks that combine data from many sources and work in real time is of the utmost importance for urban administration. The advent of sensors using IoT technology and the application of Artificial Intelligence (AI) to fuse diverse data from many sources has inspired new approaches to finding a solution to the problem of traffic data collection and monitoring. The use of new technologies, and, in particular, methods involving AI, such as deep learning, allows for large amounts of data to be processed quickly and creates new possibilities for their analysis.

This Special Issue focuses on reviewing advances in the methods and technologies used to monitor traffic in cities. We welcome submissions that present the results of studies on the application of new technologies for remote sensing and the fusion of traffic data from diverse sources.  

Original research papers or review manuscripts that focus on the following areas are invited:

  • Traffic-monitoring using UAVs (Unmanned Aerial Vehicles);
  • UAVs for the collection of traffic data;
  • Data fusion from multiple traffic sensing modalities;
  • Image-based assessment of road network congestion;
  • Road infrastructure condition monitoring;
  • The application of deep learning in urban traffic monitoring systems;
  • The Impact of IoT technology on traffic data collection.

Dr. Wiesław Pamuła
Dr. Teresa Pamuła
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban traffic monitoring
  • traffic data fusion
  • road network congestion
  • UAVs
  • image processing
  • road infrastructure
  • deep learning

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Published Papers (1 paper)

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Research

19 pages, 11796 KB  
Article
Improved Clutter Suppression and Detection of Moving Target with a Fully Polarimetric Radar
by Zhilong Zhao, Zhongkai Wen, Changhu Xue, Zhiying Cui, Xutao Hou, Haibin Zhu, Yaxin Mu, Zongqiang Liu, Zhenghuan Xia and Xin Liu
Remote Sens. 2025, 17(17), 2975; https://doi.org/10.3390/rs17172975 - 27 Aug 2025
Viewed by 465
Abstract
Remote sensing of moving targets, particularly pedestrians on the road, is crucial for advanced driver assistance systems. However, pedestrian detection using the radar system remains an ongoing challenge due to the radar cross section (RCS) of pedestrians being much smaller than that of [...] Read more.
Remote sensing of moving targets, particularly pedestrians on the road, is crucial for advanced driver assistance systems. However, pedestrian detection using the radar system remains an ongoing challenge due to the radar cross section (RCS) of pedestrians being much smaller than that of the clutter. Existing radar systems and pedestrian detection methods predominantly rely on the single-polarization radar, while research on the fully polarized radar for pedestrian detection is relatively limited. In this paper, the L-band fully polarimetric radar system is developed for pedestrian detection, and based on the full polarized radar echo HH, HV, VH, and VV, a novel clutter suppression method is proposed, which integrates the optimal polarization states of antennas and optimal scattering characteristics of pedestrians. Moreover, the field experiment has been conducted, and the results demonstrate that the signal-to-clutter-plus-noise ratio (SCNR) of the total power signal of full-polarization echoes is higher than that of single-polarization echoes, and the proposed clutter suppression method is able to reduce the non-stationary clutter and the interference signal generated by the multipath effect, thereby improving the SCNR. Furthermore, the OTSU algorithm is employed to detect pedestrian targets using radar data before and after clutter suppression, and the results demonstrate that the proposed method yields superior detection performance. These findings justify the potential of fully polarimetric radar in enhancing pedestrian detection. Full article
(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring (Second Edition))
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