Journal Browser

Journal Browser

Special Issue "Remote sensing for Intelligent Transportation Systems"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 1997

Special Issue Editors

Dr. K. Shankar
Guest Editor
School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India
Interests: secret sharing scheme; image security; IoT and healthcare applications; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the number of research activities related to the utilization of remote sensing technologies towards their implementation in transportation system is increasing tremendously. Transportation systems have become a fundamental base for the economic growth of all nations. Nevertheless, many cities around the world are facing an uncontrolled growth in traffic volume, causing serious problems, such as delays, traffic jams, higher fuel prices, increase of CO2 emissions, accidents, emergencies, and the degradation of quality of life in modern society. Advances in information and communication technologies (ICT) in areas such as hardware, software, and communications have created new opportunities for developing a sustainable, intelligent transportation system. The integration of ICT with the transportation infrastructure will enable a better, safer traveling experience and migration to intelligent transportation systems (ITS) which focus on four fundamental principles: Sustainability, integration, safety, and responsiveness. The success of ITS largely depends on the platform used to access, collect, and process accurate data from the environment. Remote sensing (both terrestrial and satellite) has been revealed to be a suitable approach to effectively collecting data at a large scale, and with an accuracy level that satisfies the ITS demand. Different advanced technologies have enabled automated modeling, and the interpretation of the data is an interesting topic of remote sensing based ITS. 

The goal of this Special Issue is to collect the recent technologies, data collection and data processing techniques, algorithms, protocols, and decision support systems related to the field of ITS in remote sensing.

Topics of interest include but are not limited to:

  • Application of advanced technologies in ITS;
  • Artificial Intelligence for ITS in remote sensing;
  • Automatic onboard processing techniques for ITS;
  • Communication technologies for ITS in remote sensing;
  • Networking resource management for ITS in remote sensing;
  • Remote sensing based ITS for smart cities;
  • Research on intelligence computing models for ITS;
  • Security and privacy in ITS;
  • Sensing technologies for ITS in remote sensing;
  • Smart decision support systems for ITS.

Dr. Mohamed Elhoseny
Dr. K. Shankar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 2500 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.


  • Application models
  • Communication technologies
  • Decision support system
  • Intelligent onboard processing system
  • Resource management
  • Security and privacy
  • ITS for smart cities

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner
Remote Sens. 2020, 12(3), 411; - 28 Jan 2020
Cited by 5 | Viewed by 1539
Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial [...] Read more.
Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters. Full article
(This article belongs to the Special Issue Remote sensing for Intelligent Transportation Systems)
Show Figures

Graphical abstract

Back to TopTop